diff --git a/tasks/crud/README.md b/tasks/crud/README.md new file mode 100644 index 0000000..a44faec --- /dev/null +++ b/tasks/crud/README.md @@ -0,0 +1,65 @@ +# CRUD Service for File Metadata + +This project provides a simple CRUD (Create, Read, Update, Delete) API for managing file metadata using Go, MongoDB, and Gorilla Mux. Below is a description of the main files in the `crud` directory: + +## File Overview + +### main.go +- Entry point for the application. +- Loads environment variables, connects to MongoDB, and sets up HTTP routes. +- Registers endpoints for health check and all file operations (create, read, update, delete). + +### usecase/filemetadata.go +- Contains the `FileService` struct and handler methods for each API endpoint. +- Implements business logic for creating, retrieving, updating, and deleting file metadata. +- Handles HTTP requests and responses. + +### repository/filemetadata.go +- Defines the `FileMetadataRepo` struct and methods for interacting with MongoDB. +- Implements database operations for file metadata (insert, find, update, delete). + +### repository/filemetadata_test.go +- Contains unit tests for the repository layer. +- Tests CRUD operations against a test MongoDB collection. + +### model/filemetadata.go +- Defines the `FileMetadata` struct representing the file metadata schema. +- Used by both the usecase and repository layers. + +## How to Run + +1. Set up a `.env` file with the following variables: + - `MONGO_URI`: MongoDB connection string + - `DB_NAME`: Database name + - `COLLECTION_NAME`: Collection name +2. Run `go mod tidy` to install dependencies. +3. Start the server: + ```sh + go run main.go + ``` +4. The API will be available at `http://localhost:4444`. + +## Endpoints +- `GET /health` — Health check +- `POST /file` — Create a new file metadata entry +- `GET /file/{id}` — Get file metadata by ID +- `GET /file` — Get all file metadata +- `PUT /file/{id}` — Update file metadata by ID +- `DELETE /file/{id}` — Delete file metadata by ID +- `DELETE /file` — Delete all file metadata + +## Dependencies +- [Gorilla Mux](https://github.com/gorilla/mux) +- [MongoDB Go Driver](https://go.mongodb.org/mongo-driver) +- [godotenv](https://github.com/joho/godotenv) + +--- + + +For more details, see comments in each file or contact the project maintainer. + +## Testing +![alt text](image.png) +![alt text](image-2.png) +![alt text](image-3.png) +![alt text](image-4.png) \ No newline at end of file diff --git a/tasks/crud/go.mod b/tasks/crud/go.mod new file mode 100644 index 0000000..2e7213f --- /dev/null +++ b/tasks/crud/go.mod @@ -0,0 +1,22 @@ +module github.com/centauri1219/filenest/tasks/crud + +go 1.24.4 + +require ( + github.com/gorilla/mux v1.8.1 + github.com/joho/godotenv v1.5.1 + go.mongodb.org/mongo-driver v1.17.4 +) + +require ( + github.com/golang/snappy v0.0.4 // indirect + github.com/klauspost/compress v1.16.7 // indirect + github.com/montanaflynn/stats v0.7.1 // indirect + github.com/xdg-go/pbkdf2 v1.0.0 // indirect + github.com/xdg-go/scram v1.1.2 // indirect + github.com/xdg-go/stringprep v1.0.4 // indirect + github.com/youmark/pkcs8 v0.0.0-20240726163527-a2c0da244d78 // indirect + golang.org/x/crypto v0.26.0 // indirect + golang.org/x/sync v0.8.0 // indirect + golang.org/x/text v0.17.0 // indirect +) diff --git a/tasks/crud/go.sum b/tasks/crud/go.sum new file mode 100644 index 0000000..f3e05a0 --- /dev/null +++ b/tasks/crud/go.sum @@ -0,0 +1,54 @@ +github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c= +github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38= +github.com/golang/snappy v0.0.4 h1:yAGX7huGHXlcLOEtBnF4w7FQwA26wojNCwOYAEhLjQM= +github.com/golang/snappy v0.0.4/go.mod h1:/XxbfmMg8lxefKM7IXC3fBNl/7bRcc72aCRzEWrmP2Q= +github.com/google/go-cmp v0.6.0 h1:ofyhxvXcZhMsU5ulbFiLKl/XBFqE1GSq7atu8tAmTRI= +github.com/google/go-cmp v0.6.0/go.mod h1:17dUlkBOakJ0+DkrSSNjCkIjxS6bF9zb3elmeNGIjoY= +github.com/gorilla/mux v1.8.1 h1:TuBL49tXwgrFYWhqrNgrUNEY92u81SPhu7sTdzQEiWY= +github.com/gorilla/mux v1.8.1/go.mod h1:AKf9I4AEqPTmMytcMc0KkNouC66V3BtZ4qD5fmWSiMQ= +github.com/joho/godotenv v1.5.1 h1:7eLL/+HRGLY0ldzfGMeQkb7vMd0as4CfYvUVzLqw0N0= +github.com/joho/godotenv v1.5.1/go.mod h1:f4LDr5Voq0i2e/R5DDNOoa2zzDfwtkZa6DnEwAbqwq4= +github.com/klauspost/compress v1.16.7 h1:2mk3MPGNzKyxErAw8YaohYh69+pa4sIQSC0fPGCFR9I= +github.com/klauspost/compress v1.16.7/go.mod h1:ntbaceVETuRiXiv4DpjP66DpAtAGkEQskQzEyD//IeE= +github.com/montanaflynn/stats v0.7.1 h1:etflOAAHORrCC44V+aR6Ftzort912ZU+YLiSTuV8eaE= +github.com/montanaflynn/stats v0.7.1/go.mod h1:etXPPgVO6n31NxCd9KQUMvCM+ve0ruNzt6R8Bnaayow= +github.com/xdg-go/pbkdf2 v1.0.0 h1:Su7DPu48wXMwC3bs7MCNG+z4FhcyEuz5dlvchbq0B0c= +github.com/xdg-go/pbkdf2 v1.0.0/go.mod h1:jrpuAogTd400dnrH08LKmI/xc1MbPOebTwRqcT5RDeI= +github.com/xdg-go/scram v1.1.2 h1:FHX5I5B4i4hKRVRBCFRxq1iQRej7WO3hhBuJf+UUySY= +github.com/xdg-go/scram v1.1.2/go.mod h1:RT/sEzTbU5y00aCK8UOx6R7YryM0iF1N2MOmC3kKLN4= +github.com/xdg-go/stringprep v1.0.4 h1:XLI/Ng3O1Atzq0oBs3TWm+5ZVgkq2aqdlvP9JtoZ6c8= +github.com/xdg-go/stringprep v1.0.4/go.mod h1:mPGuuIYwz7CmR2bT9j4GbQqutWS1zV24gijq1dTyGkM= +github.com/youmark/pkcs8 v0.0.0-20240726163527-a2c0da244d78 h1:ilQV1hzziu+LLM3zUTJ0trRztfwgjqKnBWNtSRkbmwM= +github.com/youmark/pkcs8 v0.0.0-20240726163527-a2c0da244d78/go.mod h1:aL8wCCfTfSfmXjznFBSZNN13rSJjlIOI1fUNAtF7rmI= +github.com/yuin/goldmark v1.4.13/go.mod h1:6yULJ656Px+3vBD8DxQVa3kxgyrAnzto9xy5taEt/CY= +go.mongodb.org/mongo-driver v1.17.4 h1:jUorfmVzljjr0FLzYQsGP8cgN/qzzxlY9Vh0C9KFXVw= +go.mongodb.org/mongo-driver v1.17.4/go.mod h1:Hy04i7O2kC4RS06ZrhPRqj/u4DTYkFDAAccj+rVKqgQ= +golang.org/x/crypto v0.0.0-20190308221718-c2843e01d9a2/go.mod h1:djNgcEr1/C05ACkg1iLfiJU5Ep61QUkGW8qpdssI0+w= +golang.org/x/crypto v0.0.0-20210921155107-089bfa567519/go.mod h1:GvvjBRRGRdwPK5ydBHafDWAxML/pGHZbMvKqRZ5+Abc= +golang.org/x/crypto v0.26.0 h1:RrRspgV4mU+YwB4FYnuBoKsUapNIL5cohGAmSH3azsw= +golang.org/x/crypto v0.26.0/go.mod h1:GY7jblb9wI+FOo5y8/S2oY4zWP07AkOJ4+jxCqdqn54= +golang.org/x/mod v0.6.0-dev.0.20220419223038-86c51ed26bb4/go.mod h1:jJ57K6gSWd91VN4djpZkiMVwK6gcyfeH4XE8wZrZaV4= +golang.org/x/net v0.0.0-20190620200207-3b0461eec859/go.mod h1:z5CRVTTTmAJ677TzLLGU+0bjPO0LkuOLi4/5GtJWs/s= +golang.org/x/net v0.0.0-20210226172049-e18ecbb05110/go.mod h1:m0MpNAwzfU5UDzcl9v0D8zg8gWTRqZa9RBIspLL5mdg= +golang.org/x/net v0.0.0-20220722155237-a158d28d115b/go.mod h1:XRhObCWvk6IyKnWLug+ECip1KBveYUHfp+8e9klMJ9c= +golang.org/x/sync v0.0.0-20190423024810-112230192c58/go.mod h1:RxMgew5VJxzue5/jJTE5uejpjVlOe/izrB70Jof72aM= +golang.org/x/sync 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h1:jbD1KX2456YbFQfuXm/mYQcufACuNUgVhRMnK/tPxf8= +golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ= +golang.org/x/text v0.3.3/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ= +golang.org/x/text v0.3.7/go.mod h1:u+2+/6zg+i71rQMx5EYifcz6MCKuco9NR6JIITiCfzQ= +golang.org/x/text v0.3.8/go.mod h1:E6s5w1FMmriuDzIBO73fBruAKo1PCIq6d2Q6DHfQ8WQ= +golang.org/x/text v0.17.0 h1:XtiM5bkSOt+ewxlOE/aE/AKEHibwj/6gvWMl9Rsh0Qc= +golang.org/x/text v0.17.0/go.mod h1:BuEKDfySbSR4drPmRPG/7iBdf8hvFMuRexcpahXilzY= +golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ= +golang.org/x/tools v0.0.0-20191119224855-298f0cb1881e/go.mod h1:b+2E5dAYhXwXZwtnZ6UAqBI28+e2cm9otk0dWdXHAEo= +golang.org/x/tools v0.1.12/go.mod h1:hNGJHUnrk76NpqgfD5Aqm5Crs+Hm0VOH/i9J2+nxYbc= +golang.org/x/xerrors v0.0.0-20190717185122-a985d3407aa7/go.mod h1:I/5z698sn9Ka8TeJc9MKroUUfqBBauWjQqLJ2OPfmY0= diff --git a/tasks/crud/image-2.png b/tasks/crud/image-2.png new file mode 100644 index 0000000..99481b2 Binary files /dev/null and b/tasks/crud/image-2.png differ diff --git a/tasks/crud/image-3.png b/tasks/crud/image-3.png new file mode 100644 index 0000000..f05d101 Binary files /dev/null and b/tasks/crud/image-3.png differ diff --git a/tasks/crud/image-4.png b/tasks/crud/image-4.png new file mode 100644 index 0000000..ce9a2fa Binary files /dev/null and b/tasks/crud/image-4.png differ diff --git a/tasks/crud/image.png b/tasks/crud/image.png new file mode 100644 index 0000000..cb55586 Binary files /dev/null and b/tasks/crud/image.png differ diff --git a/tasks/crud/main.go b/tasks/crud/main.go new file mode 100644 index 0000000..0bef97d --- /dev/null +++ b/tasks/crud/main.go @@ -0,0 +1,65 @@ +package main + +import ( + "context" + "log" + "net/http" + "os" + + "github.com/centauri1219/filenest/tasks/crud/usecase" + "github.com/gorilla/mux" + "github.com/joho/godotenv" + "go.mongodb.org/mongo-driver/mongo" + "go.mongodb.org/mongo-driver/mongo/options" + "go.mongodb.org/mongo-driver/mongo/readpref" +) + +var mongoClient *mongo.Client + +func init() { //initiate the mongo conneciton + //load .env file + err := godotenv.Load() + if err != nil { + log.Fatal("Error loading .env file", err) + } + log.Println("Environment variables loaded successfully ") + + //create mongo client + mongoClient, err = mongo.Connect(context.Background(), options.Client().ApplyURI(os.Getenv("MONGO_URI"))) //mongo connect only tells if the connection string is correct + //context.background() is used to create a context for the connection, which can be used to cancel the operation if needed and manage its lifetime + if err != nil { + log.Fatal("Error connecting to MongoDB", err) + } + + err = mongoClient.Ping(context.Background(), readpref.Primary()) //sends request to mongo client to verify if it can connect to deployment + if err != nil { + log.Fatal("Error pinging MongoDB", err) + } + + log.Println("MongoDB connected successfully") +} + +func main() { + defer mongoClient.Disconnect(context.Background()) //close mongo connection + coll := mongoClient.Database(os.Getenv("DB_NAME")).Collection(os.Getenv("COLLECTION_NAME")) //coll is a reference to the mongodb collection using mongoclient + //create file service + fileservice := usecase.FileService{MongoCollection: coll} //fileservice is an object that provides file management logic, backed by mongodb + + //create router + r := mux.NewRouter() //directs incoming http requests to the handler funcs + r.HandleFunc("/health", healthHandler).Methods(http.MethodGet) //check if server is running + r.HandleFunc("/file", fileservice.CreateFile).Methods(http.MethodPost) + r.HandleFunc("/file/{id}", fileservice.GetFilebyID).Methods(http.MethodGet) + r.HandleFunc("/file", fileservice.GetAllFiles).Methods(http.MethodGet) + r.HandleFunc("/file/{id}", fileservice.UpdateFilebyID).Methods(http.MethodPut) + r.HandleFunc("/file/{id}", fileservice.DeleteFilebyID).Methods(http.MethodDelete) + r.HandleFunc("/file", fileservice.DeleteAll).Methods(http.MethodDelete) + + log.Println("Server is running on port 4444") + http.ListenAndServe(":4444", r) +} + +func healthHandler(w http.ResponseWriter, r *http.Request) { //mapped to /health endpoint + w.WriteHeader(http.StatusOK) //http response + w.Write([]byte("running....")) //write response to client +} diff --git a/tasks/crud/model/filemetadata.go b/tasks/crud/model/filemetadata.go new file mode 100644 index 0000000..132ebed --- /dev/null +++ b/tasks/crud/model/filemetadata.go @@ -0,0 +1,15 @@ +package model + +import ( + "time" +) + +type FileMetadata struct { + ID int `json:"id" bson:"id"` + FileName string `json:"filename" bson:"filename"` + FilePath string `json:"filepath" bson:"filepath"` + FileSize int64 `json:"filesize" bson:"filesize"` + ContentType string `json:"content_type" bson:"content_type"` + CreatedAt time.Time `json:"created_at" bson:"created_at"` + UpdatedAt time.Time `json:"updated_at" bson:"updated_at"` +} diff --git a/tasks/crud/repository/filemetadata.go b/tasks/crud/repository/filemetadata.go new file mode 100644 index 0000000..e5c9eec --- /dev/null +++ b/tasks/crud/repository/filemetadata.go @@ -0,0 +1,84 @@ +package repository + +import ( + "context" + "fmt" + + "github.com/centauri1219/filenest/tasks/crud/model" + "go.mongodb.org/mongo-driver/bson" + "go.mongodb.org/mongo-driver/mongo" +) + +type FileMetadataRepo struct { //repository layer + MongoCollection *mongo.Collection //holds a reference to the MongoDB collection on which we point our operations + +} + +func (r *FileMetadataRepo) InsertFile(fil *model.FileMetadata) (interface{}, error) { //interface is the object id thatll be created by mongodb + result, err := r.MongoCollection.InsertOne(context.Background(), fil) //again, context.background() is used to create a context for the operation, which can be used to cancel the operation if needed and manage its lifetime + if err != nil { + return nil, err + } + + return result.InsertedID, nil +} + +func (r *FileMetadataRepo) FindFilebyID(fileID int) (*model.FileMetadata, error) { + var file model.FileMetadata // we have to covert bson to struct field model.filemetadata + err := r.MongoCollection.FindOne(context.Background(), + bson.D{{Key: "id", Value: fileID}}).Decode(&file) + + if err != nil { + return nil, err + } + + return &file, nil + +} + +func (r *FileMetadataRepo) FindAllFiles() ([]model.FileMetadata, error) { + results, err := r.MongoCollection.Find(context.Background(), bson.D{}) //creates cursor to iterate over all documents in the collection + if err != nil { + return nil, err + } + var files []model.FileMetadata + err = results.All(context.Background(), &files) //decodes and stores in files slice + if err != nil { + return nil, fmt.Errorf("results decode error %s", err.Error()) + } + + return files, nil +} + +func (r *FileMetadataRepo) UpdateFilebyID(fileID int, updatefile *model.FileMetadata) (int64, error) { + result, err := r.MongoCollection.UpdateOne(context.Background(), + bson.D{{Key: "id", Value: fileID}}, //old file metadata will be searched by id + bson.D{{Key: "$set", Value: updatefile}}) //new file metadata will replace the old one + + if err != nil { + return 0, err + } + + return result.ModifiedCount, nil //modified count: if you update the same doc again and again itll return 0 +} + +func (r *FileMetadataRepo) DeleteFilebyID(fileID int) (int64, error) { + result, err := r.MongoCollection.DeleteOne(context.Background(), + bson.D{{Key: "id", Value: fileID}}) + if err != nil { + return 0, err + } + + return result.DeletedCount, nil +} + +func (r *FileMetadataRepo) DeleteAll() (int64, error) { + results, err := r.MongoCollection.DeleteMany(context.Background(), + bson.D{{}}) + + if err != nil { + return 0, err + } + + return results.DeletedCount, nil +} diff --git a/tasks/crud/repository/filemetadata_test.go b/tasks/crud/repository/filemetadata_test.go new file mode 100644 index 0000000..95e6e2e --- /dev/null +++ b/tasks/crud/repository/filemetadata_test.go @@ -0,0 +1,152 @@ +package repository + +//test functions of filemetadata (repository) +import ( + "context" + "log" + "math/rand" + "testing" + + "github.com/centauri1219/filenest/tasks/crud/model" + "go.mongodb.org/mongo-driver/mongo" + "go.mongodb.org/mongo-driver/mongo/options" + "go.mongodb.org/mongo-driver/mongo/readpref" +) + +func newMongoClient() *mongo.Client { + mongoTestClient, err := mongo.Connect(context.Background(), + options.Client().ApplyURI("mongodb+srv://admin:49c14IwyVUTJsqkE@cluster0.4k6j8iy.mongodb.net/?retryWrites=true&w=majority&appName=Cluster0")) + + if err != nil { + log.Fatal("error while connecting mongodb", err) + } + + log.Println("Mongodb succesfully connected") + + err = mongoTestClient.Ping(context.Background(), readpref.Primary()) + + if err != nil { + log.Fatal("ping failed", err) + } + + log.Println("ping success") + + return mongoTestClient +} + +func TestMongoOpearations(t *testing.T) { + mongoTestClient := newMongoClient() + defer mongoTestClient.Disconnect(context.Background()) + + //dummy data + file1 := rand.Int() + file2 := rand.Int() + + // we have just created client we have to connect to our collection + coll := mongoTestClient.Database("fileinfodb").Collection("files_test") + + filerepo := FileMetadataRepo{MongoCollection: coll} //test repository object + + //Insert 1 file data + t.Run("Insert File 1", func(t *testing.T) { + fil := model.FileMetadata{ + ID: file1, + FileName: "Testfile1", + FilePath: "C:/men", + FileSize: 6969, + ContentType: ".txt", + //leaving time shit empty + } + result, err := filerepo.InsertFile(&fil) + if err != nil { + t.Fatal("insert 1 opertaion failed", err) + } + t.Log("Insert 1 success", result) + }) + + t.Run("Insert File 2", func(t *testing.T) { + fil := model.FileMetadata{ + ID: file2, + FileName: "Testfile2", + FilePath: "C:/men2", + FileSize: 6969, + ContentType: ".txt", + //leaving time shit empty + } + result, err := filerepo.InsertFile(&fil) + if err != nil { + t.Fatal("insert 2 opertaion failed", err) + } + t.Log("Insert 2 success", result) + }) + + //get file 1 data + t.Run("Get File 1", func(t *testing.T) { + result, err := filerepo.FindFilebyID(file1) + + if err != nil { + t.Fatal("get operation failed", err) + } + t.Log("Get File 1 success", result.FileName) + }) + + t.Run("Get all files", func(t *testing.T) { + results, err := filerepo.FindAllFiles() + + if err != nil { + t.Fatal("get all operation failed", err) + } + t.Log("Get all files success", results) + }) + + t.Run("Update File1 name", func(t *testing.T) { + fil := model.FileMetadata{ + ID: file1, + FileName: "UpdatedTestfile1", + FilePath: "C:/men", + FileSize: 6969, + ContentType: ".txt", + } + result, err := filerepo.UpdateFilebyID(file1, &fil) + if err != nil { + t.Fatal("update operation failed", err) + } + t.Log("Update File1 name success", result) + }) + + t.Run("Get file 1 after update", func(t *testing.T) { + result, err := filerepo.FindFilebyID(file1) + + if err != nil { + t.Fatal("get operation after update failed", err) + } + t.Log("Get File 1 after update success", result.FileName) + }) + + //delete file 1 + t.Run("Delete File 1", func(t *testing.T) { + result, err := filerepo.DeleteFilebyID(file1) + if err != nil { + log.Fatal("delete operation failed", err) + } + t.Log("Delete File 1 success", result) + }) + + t.Run("Get all files after delete", func(t *testing.T) { + results, err := filerepo.FindAllFiles() + if err != nil { + log.Fatal("get all operation failed", err) + } + t.Log("Get all files after delete success", results) + }) + + //delete all files + t.Run("Delete all files for cleanup", func(t *testing.T) { + result, err := filerepo.DeleteAll() + if err != nil { + log.Fatal("delete all operation failed", err) + } + + t.Log("Delete all files success", result) + }) +} diff --git a/tasks/crud/usecase/filemetadata.go b/tasks/crud/usecase/filemetadata.go new file mode 100644 index 0000000..a862d3b --- /dev/null +++ b/tasks/crud/usecase/filemetadata.go @@ -0,0 +1,200 @@ +package usecase + +//controls what happens on the local server when a request is made to the file service +import ( + "encoding/json" + "log" + "math/rand" + "net/http" + "strconv" + + "github.com/centauri1219/filenest/tasks/crud/model" + "github.com/centauri1219/filenest/tasks/crud/repository" + "github.com/gorilla/mux" + "go.mongodb.org/mongo-driver/mongo" +) + +type FileService struct { + MongoCollection *mongo.Collection +} + +type Response struct { + Data interface{} `json:"data,omitempty"` + Error string `json:"error,omitempty"` +} + +func (svc *FileService) CreateFile(w http.ResponseWriter, r *http.Request) { + w.Header().Add("Content-Type", "application/json") //indicates JSON output + res := &Response{} //to hold result or error + defer json.NewEncoder(w).Encode(res) + + var fil model.FileMetadata + + err := json.NewDecoder(r.Body).Decode(&fil) + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("invalid body", err) + res.Error = err.Error() + return + } + + //assign new fileid + fil.ID = rand.Int() //for simplicity, using a random number as file ID + + repo := repository.FileMetadataRepo{ + MongoCollection: svc.MongoCollection} + + //inser file + insertID, err := repo.InsertFile(&fil) //calls repository function to insert file metadata into the database + if err != nil { + w.WriteHeader(http.StatusInternalServerError) + log.Println("error while inserting file", err) + res.Error = err.Error() + return + } + + res.Data = fil.ID + w.WriteHeader(http.StatusOK) + log.Println("file created successfully with ID:", insertID, fil) +} + +func (svc *FileService) GetFilebyID(w http.ResponseWriter, r *http.Request) { + w.Header().Add("Content-Type", "application/json") + res := &Response{} + defer json.NewEncoder(w).Encode(res) + //get file id + fileIDStr := mux.Vars(r)["id"] //extracts the file ID from the URL path + fileID, err := strconv.Atoi(fileIDStr) //convert to integer + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("invalid file id", err) + res.Error = "invalid file id" + return + } + log.Println("file id:", fileID) + repo := repository.FileMetadataRepo{ + MongoCollection: svc.MongoCollection} + + fil, err := repo.FindFilebyID(fileID) + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("error while getting file by ID", err) + res.Error = err.Error() + return + } + + res.Data = fil + w.WriteHeader(http.StatusOK) + +} +func (svc *FileService) GetAllFiles(w http.ResponseWriter, r *http.Request) { + w.Header().Add("Content-Type", "application/json") + res := &Response{} + defer json.NewEncoder(w).Encode(res) + repo := repository.FileMetadataRepo{ + MongoCollection: svc.MongoCollection} + + fil, err := repo.FindAllFiles() + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("error while getting file by ID", err) + res.Error = err.Error() + return + } + + res.Data = fil + w.WriteHeader(http.StatusOK) +} +func (svc *FileService) UpdateFilebyID(w http.ResponseWriter, r *http.Request) { + w.Header().Add("Content-Type", "application/json") + res := &Response{} + defer json.NewEncoder(w).Encode(res) + + //get file id + fileIDStr := mux.Vars(r)["id"] + fileID, err := strconv.Atoi(fileIDStr) + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("invalid file id", err) + res.Error = "invalid file id" + return + } + log.Println("file id:", fileID) + if fileIDStr == "" { + w.WriteHeader(http.StatusBadRequest) + log.Println("file ID is required") + res.Error = "file ID is required" + return + } + + var fil model.FileMetadata + err = json.NewDecoder(r.Body).Decode(&fil) + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("invalid body", err) + res.Error = err.Error() + return + } + + fil.ID = fileID //assign the file ID from the URL + repo := repository.FileMetadataRepo{MongoCollection: svc.MongoCollection} + count, err := repo.UpdateFilebyID(fileID, &fil) + if err != nil { + w.WriteHeader(http.StatusInternalServerError) + log.Println("error while updating file", err) + res.Error = err.Error() + return + } + res.Data = count + w.WriteHeader(http.StatusOK) +} + +func (svc *FileService) DeleteFilebyID(w http.ResponseWriter, r *http.Request) { + w.Header().Add("Content-Type", "application/json") + res := &Response{} + defer json.NewEncoder(w).Encode(res) + + //get file id + fileIDStr := mux.Vars(r)["id"] + fileID, err := strconv.Atoi(fileIDStr) + if err != nil { + w.WriteHeader(http.StatusBadRequest) + log.Println("invalid file id", err) + res.Error = "invalid file id" + return + } + log.Println("file id:", fileID) + + repo := repository.FileMetadataRepo{ + MongoCollection: svc.MongoCollection} + count, err := repo.DeleteFilebyID(fileID) + if err != nil { + w.WriteHeader(http.StatusInternalServerError) + log.Println("error while deleting file", err) + res.Error = err.Error() + return + } + + res.Data = count + w.WriteHeader(http.StatusOK) +} +func (svc *FileService) DeleteAll(w http.ResponseWriter, r *http.Request) { + w.Header().Add("Content-Type", "application/json") + res := &Response{} + defer json.NewEncoder(w).Encode(res) + + //get file id + + repo := repository.FileMetadataRepo{ + MongoCollection: svc.MongoCollection} + count, err := repo.DeleteAll() + if err != nil { + w.WriteHeader(http.StatusInternalServerError) + log.Println("error while deleting file", err) + res.Error = err.Error() + return + } + + res.Data = count + w.WriteHeader(http.StatusOK) +} diff --git a/tasks/mnistclass/README.md b/tasks/mnistclass/README.md new file mode 100644 index 0000000..2a64eee --- /dev/null +++ b/tasks/mnistclass/README.md @@ -0,0 +1,215 @@ +# MNIST Digit Recognition with <10K Parameters + +A lightweight MNIST digit classifier that achieves >98.5% accuracy with fewer than 10,000 parameters in under 20 epochs. + +## 🎯 Project Goals + +- ✅ Model with <10,000 parameters +- ✅ >98.5% accuracy on test set +- ✅ <20 epochs training time +- ✅ PyTorch implementation +- ✅ Wandb experiment logging +- ✅ Hydra configuration management +- ✅ Ray hyperparameter tuning +- ✅ UV package management + +## 🏗️ Architecture + +The model uses depthwise separable convolutions to minimize parameters while maintaining accuracy: + +- **Depthwise Separable Convolutions**: Reduces parameters by ~8x compared to standard convolutions +- **Global Average Pooling**: Eliminates large fully connected layers +- **Batch Normalization**: Improves training stability +- **Strategic Channel Progression**: 1→16→32→64→32→10 channels + +**Total Parameters**: ~9730 (<10K limit) + +## 📁 Project Structure + +``` +mnist_project/ +├── configs/ +│ ├── config.yaml # Main configuration +│ ├── model/ +│ │ └── lightweight_cnn.yaml # Model architecture config +│ ├── optimizer/ +│ │ └── adam.yaml # Optimizer settings +│ └── dataset/ +│ └── mnist.yaml # Dataset configuration +├── src/ +│ ├── __init__.py +│ ├── model.py # Lightweight CNN model +│ ├── dataset.py # Data loading utilities +│ ├── trainer.py # Training loop +│ └── utils.py # Helper functions +├── train.py # Main training script +├── evaluate.py # Model evaluation +├── tune.py # Hyperparameter tuning +├── pyproject.toml # Dependencies & project config +└── README.md +``` + +## 🚀 Quick Start + +### 1. Install Dependencies + +```bash +# Install uv package manager +curl -LsSf https://astral.sh/uv/install.sh | sh + +# Install dependencies +uv sync +``` + +### 2. Set Up Wandb + +```bash +# Login to wandb (optional but recommended) +uv run wandb login + +# Or set your API key +export WANDB_API_KEY="your_api_key" +``` + +### 3. Train the Model + +```bash +# Basic training +uv run python train.py + +# With custom config +uv run python train.py training.epochs=15 training.learning_rate=0.002 +``` + +### 4. Evaluate the Model + +```bash +uv run python evaluate.py +``` + +## 📊 Expected Results + +- **Parameters**: ~9,730 (< 10,000 ✅) +- **Test Accuracy**: >98.5% ✅ +- **Training Time**: 10-15 epochs ✅ +- **GPU Memory**: <1GB +- **Training Time**: ~2-3 minutes on GPU + +## 🔧 Configuration + +### Model Architecture +```yaml +# configs/model/lightweight_cnn.yaml +base_channels: 16 # Starting channel count +dropout_rate: 0.2 # Dropout probability +use_batch_norm: true # Enable batch normalization +use_global_avg_pool: true # Use global average pooling +``` + +### Training Settings +```yaml +# configs/config.yaml +training: + epochs: 20 + batch_size: 128 + learning_rate: 0.001 + device: cuda +``` + +### Wandb Logging +```yaml +wandb: + project: "mnist-recognition" + entity: null # Set to your username + tags: ["mnist", "lightweight", "pytorch"] +``` + +## 📈 Monitoring + +The training automatically logs to Wandb: + +- Training/validation loss and accuracy +- Learning rate schedule +- Model parameter count +- Gradient histograms +- Confusion matrix +- Sample predictions + +## 🎛️ Hyperparameter Tuning + +The tuning script explores: + +- **Learning Rate**: 1e-4 to 1e-2 (log scale) +- **Batch Size**: [64, 128, 256] +- **Dropout Rate**: 0.1 to 0.5 +- **Base Channels**: [12, 16, 20, 24] +- **Weight Decay**: 1e-5 to 1e-3 (log scale) + +Results are automatically saved and integrated with Wandb. + +## 🧪 Key Implementation Details + +### Parameter Efficiency +- **Depthwise Separable Conv**: 3x3 depthwise + 1x1 pointwise +- **Global Average Pooling**: Replaces large FC layers +- **Minimal Channel Growth**: Strategic channel progression + +### Training Optimizations +- **Learning Rate Scheduling**: StepLR with γ=0.1 every 10 epochs +- **Early Stopping**: Stops when accuracy > 98.5% +- **Gradient Clipping**: Prevents exploding gradients + +### Data Pipeline +- **Normalization**: Mean=0.1307, Std=0.3081 (MNIST standard) +- **Efficient Loading**: DataLoader with num_workers and pin_memory +- **No Augmentation**: Achieves target without data augmentation + +## Results + +![alt text](run_images/image.png) +![alt text](run_images/image-1.png) +![alt text](run_images/image-2.png) + +## 🐛 Troubleshooting + +### Common Issues + +1. **CUDA Out of Memory** + ```bash + # Reduce batch size + uv run python train.py training.batch_size=64 + ``` + +2. **Wandb Login Issues** + ```bash + # Set API key directly + export WANDB_API_KEY="your_key" + ``` + +3. **Parameter Count Too High** + ```bash + # Reduce base channels + uv run python train.py model.base_channels=12 + ``` + +## 📋 Validation Checklist + +- [x] Model has <10,000 parameters (confirmed with torchinfo) +- [x] Achieves >98.5% test accuracy +- [x] Trains in <20 epochs +- [x] Wandb logging integrated +- [x] Hydra configuration management +- [x] Ray hyperparameter tuning +- [x] Code formatted with ruff +- [x] Modular project structure + +## 🤝 Contributing + +1. Install development dependencies: `uv sync --dev` +2. Format code: `uv run ruff format .` +3. Lint code: `uv run ruff check .` +4. Run tests: `uv run pytest` + +## 📜 License + +MIT License - see LICENSE file for details. diff --git a/tasks/mnistclass/checkpoints/best_model.pth b/tasks/mnistclass/checkpoints/best_model.pth new file mode 100644 index 0000000..2aa1699 Binary files /dev/null and b/tasks/mnistclass/checkpoints/best_model.pth differ diff --git a/tasks/mnistclass/checkpoints/checkpoint.pth b/tasks/mnistclass/checkpoints/checkpoint.pth new file mode 100644 index 0000000..9d4f86f Binary files /dev/null and b/tasks/mnistclass/checkpoints/checkpoint.pth differ diff --git a/tasks/mnistclass/configs/config.yaml b/tasks/mnistclass/configs/config.yaml new file mode 100644 index 0000000..ce00d2e --- /dev/null +++ b/tasks/mnistclass/configs/config.yaml @@ -0,0 +1,23 @@ +# Main configuration file +defaults: + - model: lightweight_cnn + - optimizer: adam + - dataset: mnist + +# Training parameters +training: + epochs: 20 + batch_size: 128 + learning_rate: 0.001 + device: cuda + save_dir: "./checkpoints" + +# Wandb configuration +wandb: + project: "mnist-recognition" + entity: null # Set to your wandb username + tags: ["mnist", "lightweight", "pytorch"] + +# General settings +seed: 42 +num_workers: 4 \ No newline at end of file diff --git a/tasks/mnistclass/configs/dataset/mnist.yaml b/tasks/mnistclass/configs/dataset/mnist.yaml new file mode 100644 index 0000000..e60da13 --- /dev/null +++ b/tasks/mnistclass/configs/dataset/mnist.yaml @@ -0,0 +1,14 @@ +# MNIST dataset configuration +name: "MNIST" +data_dir: "./data" +download: true + +# Data preprocessing +normalize: + mean: [0.1307] + std: [0.3081] + +# Data loading +batch_size: 128 +shuffle: true +pin_memory: true \ No newline at end of file diff --git a/tasks/mnistclass/configs/model/lightweight_cnn.yaml b/tasks/mnistclass/configs/model/lightweight_cnn.yaml new file mode 100644 index 0000000..6a0cc2f --- /dev/null +++ b/tasks/mnistclass/configs/model/lightweight_cnn.yaml @@ -0,0 +1,12 @@ +# Lightweight CNN model configuration +_target_: src.model.LightweightCNN + +# Model architecture parameters +in_channels: 1 +num_classes: 10 +base_channels: 15 +dropout_rate: 0.2 + +# Architecture details +use_batch_norm: true +use_global_avg_pool: true \ No newline at end of file diff --git a/tasks/mnistclass/configs/optimizer/adam.yaml b/tasks/mnistclass/configs/optimizer/adam.yaml new file mode 100644 index 0000000..cc96dcf --- /dev/null +++ b/tasks/mnistclass/configs/optimizer/adam.yaml @@ -0,0 +1,6 @@ +# Adam optimizer configuration +_target_: torch.optim.Adam + +lr: 0.001 +weight_decay: 1e-4 +betas: [0.9, 0.999] \ No newline at end of file diff --git a/tasks/mnistclass/evaluate.py b/tasks/mnistclass/evaluate.py new file mode 100644 index 0000000..bee3936 --- /dev/null +++ b/tasks/mnistclass/evaluate.py @@ -0,0 +1,125 @@ +#evaluate perfromance on test + +import hydra +from omegaconf import DictConfig +import torch +import torch.nn.functional as F +from tqdm import tqdm +import numpy as np +import matplotlib.pyplot as plt +from sklearn.metrics import classification_report, confusion_matrix +import seaborn as sns + +from src.model import create_model +from src.dataset import get_dataloaders +from src.utils import set_seed + + +@hydra.main(version_base=None, config_path="configs", config_name="config") +def main(cfg: DictConfig) -> None: + + print("Evaluatiion started") + + # Set seed + set_seed(cfg.seed) + + # Get device + device = torch.device(cfg.training.device if torch.cuda.is_available() else "cpu") + print(f"Using device: {device}") + + # Load data + _, test_loader = get_dataloaders(cfg) + + # Create model + model = create_model(cfg) + model.to(device) + + # Load best model + checkpoint_path = f"{cfg.training.save_dir}/best_model.pth" + try: + checkpoint = torch.load(checkpoint_path, map_location=device) + + model.load_state_dict(checkpoint["model_state_dict"]) + print(f"loaded model from {checkpoint_path}") + print(f"model trained for {checkpoint['epoch']} epochs") + print(f"Best accuracy during training: {checkpoint['accuracy']:.2f}%") + except FileNotFoundError: + print(f"No checkpoint found at {checkpoint_path}") + print("model not trained") + return + + # evaluate model + model.eval() + correct = 0 + total = 0 + all_predictions = [] + all_targets = [] + test_loss = 0 + + print("running evaluation.") + with torch.no_grad(): + for data, target in tqdm(test_loader): + data, target = data.to(device), target.to(device) + + output = model(data) + test_loss += F.cross_entropy(output, target, reduction='sum').item() + + pred = output.argmax(dim=1) + correct += pred.eq(target).sum().item() + total += target.size(0) + + all_predictions.extend(pred.cpu().numpy()) + all_targets.extend(target.cpu().numpy()) + + # Calculate metrics + accuracy = 100. * correct / total + avg_loss = test_loss / total + + print(f"\nTest Results:") + print(f"Average loss: {avg_loss:.4f}") + print(f"Accuracy: {correct}/{total} ({accuracy:.2f}%)") + + # Check if target met + # if accuracy >= 98.5: + # print("Target accuracy of 98.5% achieved!") + # else: + # print(f"Target accuracy not reached. Current: {accuracy:.2f}%") + + # Detailed classification report + print("\nClassification Report:") + print(classification_report(all_targets, all_predictions)) + + # Confusion matrix + cm = confusion_matrix(all_targets, all_predictions) + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues') + plt.title(f'Confusion Matrix (Accuracy: {accuracy:.2f}%)') + plt.ylabel('True Label') + plt.xlabel('Predicted Label') + + + plt.savefig('confusion_matrix.png', dpi=300, bbox_inches='tight') + print("Confusion matrix saved as confusion_matrix.png") + + # Sample predictions visualization + print("\nSample predictions:") + model.eval() + with torch.no_grad(): + # Get first batch + data, target = next(iter(test_loader)) + data, target = data.to(device), target.to(device) + output = model(data) + pred = output.argmax(dim=1) + + # Show first 10 samples + for i in range(min(10, len(data))): + actual = target[i].item() + predicted = pred[i].item() + confidence = F.softmax(output[i], dim=0)[predicted].item() + status = "✅" if actual == predicted else "❌" + print(f"Sample {i+1}: Actual={actual}, Predicted={predicted}, " + f"Confidence={confidence:.3f} {status}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tasks/mnistclass/pyproject.toml b/tasks/mnistclass/pyproject.toml new file mode 100644 index 0000000..4ad10f0 --- /dev/null +++ b/tasks/mnistclass/pyproject.toml @@ -0,0 +1,39 @@ +[project] +name = "mnist-recognition" +version = "0.1.0" +description = "MNIST digit recognition with <10k parameters" +authors = [ + {name = "Your Name", email = "your.email@example.com"}, +] +dependencies = [ + "torch>=2.0.0", + "torchvision>=0.15.0", + "wandb>=0.15.0", + "hydra-core>=1.3.0", + "ray[all,tune,wandb]>=2.5.0", + "torchinfo>=1.8.0", + "numpy>=1.24.0", + "tqdm>=4.65.0", +] +readme = "README.md" +requires-python = ">= 3.9" + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.ruff] +line-length = 88 +target-version = "py39" + +[tool.ruff.lint] +select = ["E", "F", "W", "C90", "I", "N", "UP", "YTT", "S", "BLE", "FBT", "B", "A", "COM", "C4", "DTZ", "T10", "EM", "EXE", "FA", "ISC", "ICN", "G", "INP", "PIE", "T20", "PYI", "PT", "Q", "RSE", "RET", "SLF", "SLOT", "SIM", "TID", "TCH", "INT", "ARG", "PTH", "TD", "FIX", "ERA", "PD", "PGH", "PL", "TRY", "FLY", "NPY", "AIR", "PERF"] +ignore = ["COM812", "ISC001"] + +[tool.ruff.format] +quote-style = "double" +indent-style = "space" +skip-magic-trailing-comma = false +line-ending = "auto" +[tool.hatchling.build.targets.wheel] +packages = ["."] diff --git a/tasks/mnistclass/run_images/image-1.png b/tasks/mnistclass/run_images/image-1.png new file mode 100644 index 0000000..d986d94 Binary files /dev/null and b/tasks/mnistclass/run_images/image-1.png differ diff --git a/tasks/mnistclass/run_images/image-2.png b/tasks/mnistclass/run_images/image-2.png new file mode 100644 index 0000000..995b068 Binary files /dev/null and b/tasks/mnistclass/run_images/image-2.png differ diff --git a/tasks/mnistclass/run_images/image.png b/tasks/mnistclass/run_images/image.png new file mode 100644 index 0000000..2ff78a9 Binary files /dev/null and b/tasks/mnistclass/run_images/image.png differ diff --git a/tasks/mnistclass/setup.sh b/tasks/mnistclass/setup.sh new file mode 100644 index 0000000..2b6590d --- /dev/null +++ b/tasks/mnistclass/setup.sh @@ -0,0 +1,64 @@ +#!/bin/bash + +# MNIST Recognition Project Setup Script + +echo "Setting up MNIST Recognition Project..." + +# Check if uv is installed +if ! command -v uv &> /dev/null; then + echo "Installing uv package manager..." + curl -LsSf https://astral.sh/uv/install.sh | sh + source $HOME/.cargo/env +fi + +# Initialize project +echo " Initializing project..." +uv sync + +# Create necessary directories +echo "Creating directories..." +mkdir -p data +mkdir -p checkpoints +mkdir -p ray_results + +# Check Python version +echo "Python version:" +uv run python --version + +# Verify installations +echo "Verifying installations..." + +echo "Checking PyTorch..." +uv run python -c "import torch; print(f'PyTorch: {torch.__version__}')" + +echo "Checking CUDA availability..." +uv run python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')" + +echo "Checking other dependencies..." +uv run python -c " +import wandb +import hydra +import ray +from torchinfo import summary +print('All dependencies installed successfully!') +" + +# Test model creation +echo "Testing model creation..." +uv run python -c " +from src.model import LightweightCNN +model = LightweightCNN() +params = model.count_parameters() +print(f'Model parameters: {params:,}') +if params < 10000: + print('Parameter constraint satisfied!') +else: + print('Too many parameters!') +" + +echo "Setup complete!" +echo "" +echo "Next steps:" +echo "1. Set up wandb: uv run wandb login" +echo "2. Train model: uv run python train.py" +echo "3. Evaluate: uv run python evaluate.py" diff --git a/tasks/mnistclass/src/__init__.py b/tasks/mnistclass/src/__init__.py new file mode 100644 index 0000000..33e3a94 --- /dev/null +++ b/tasks/mnistclass/src/__init__.py @@ -0,0 +1,18 @@ +#just imports key components of the package + +from .model import LightweightCNN, create_model +from .dataset import get_dataloaders, get_dataset_info +from .trainer import Trainer +from .utils import set_seed, init_wandb, count_parameters, get_device + +__all__ = [ + "LightweightCNN", + "create_model", + "get_dataloaders", + "get_dataset_info", + "Trainer", + "set_seed", + "init_wandb", + "count_parameters", + "get_device", +] \ No newline at end of file diff --git a/tasks/mnistclass/src/dataset.py b/tasks/mnistclass/src/dataset.py new file mode 100644 index 0000000..a0cb411 --- /dev/null +++ b/tasks/mnistclass/src/dataset.py @@ -0,0 +1,66 @@ +#loading and prepping MNIST +import torch +from torch.utils.data import DataLoader +from torchvision import datasets, transforms + + +def get_transforms(cfg):#normalise the data + train_transforms = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(cfg.dataset.normalize.mean, cfg.dataset.normalize.std), + ]) + + test_transforms = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(cfg.dataset.normalize.mean, cfg.dataset.normalize.std), + ]) + + return train_transforms, test_transforms + + +def get_dataloaders(cfg):#loads mnist data and creates dataloaders based on the config + train_transforms, test_transforms = get_transforms(cfg) + + # Load datasets + train_dataset = datasets.MNIST( + root=cfg.dataset.data_dir, + train=True, + download=cfg.dataset.download, + transform=train_transforms, + ) + + test_dataset = datasets.MNIST( + root=cfg.dataset.data_dir, + train=False, + download=cfg.dataset.download, + transform=test_transforms, + ) + + # Create dataloaders + train_loader = DataLoader( + train_dataset, + batch_size=cfg.training.batch_size, + shuffle=cfg.dataset.shuffle, + num_workers=cfg.num_workers, + pin_memory=cfg.dataset.pin_memory, + ) + + test_loader = DataLoader( + test_dataset, + batch_size=cfg.training.batch_size, + shuffle=False, + num_workers=cfg.num_workers, + pin_memory=cfg.dataset.pin_memory, + ) + + return train_loader, test_loader + + +def get_dataset_info(): + #dataset info + return { + "num_classes": 10, + "input_shape": (1, 28, 28), + "train_size": 60000, + "test_size": 10000, + } \ No newline at end of file diff --git a/tasks/mnistclass/src/mnist_recognition/__init__.py b/tasks/mnistclass/src/mnist_recognition/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tasks/mnistclass/src/model.py b/tasks/mnistclass/src/model.py new file mode 100644 index 0000000..b6a67d9 --- /dev/null +++ b/tasks/mnistclass/src/model.py @@ -0,0 +1,128 @@ +#this file defines the NN architecture +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class DepthwiseSeparableConv(nn.Module):#preferrred depthwise seperable instead of MobileNetv2 + #helps reduce parameters by separating depthwise and pointwise convolutions + + def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1): + super().__init__() + self.depthwise = nn.Conv2d( + in_channels, in_channels, kernel_size, stride, padding, groups=in_channels + ) + self.pointwise = nn.Conv2d(in_channels, out_channels, 1) + + def forward(self, x): + x = self.depthwise(x) + x = self.pointwise(x) + return x + + +class LightweightCNN(nn.Module):#main cnn model + + + def __init__( + self, + in_channels=1, + num_classes=10, + base_channels=21, + dropout_rate=0.2, + use_batch_norm=True,#centres activations around zero + use_global_avg_pool=True,#replaces fully connected layer with global average pooling + ): + super().__init__() + + self.use_batch_norm = use_batch_norm + self.use_global_avg_pool = use_global_avg_pool + + # First conv block + self.conv1 = nn.Conv2d(in_channels, base_channels, 3, padding=1) + self.bn1 = nn.BatchNorm2d(base_channels) if use_batch_norm else nn.Identity() + + # Depthwise separable conv blocks + self.conv2 = DepthwiseSeparableConv(base_channels, base_channels * 2) + self.bn2 = nn.BatchNorm2d(base_channels * 2) if use_batch_norm else nn.Identity() + + self.conv3 = DepthwiseSeparableConv(base_channels * 2, base_channels * 4) + self.bn3 = nn.BatchNorm2d(base_channels * 4) if use_batch_norm else nn.Identity() + + # Additional depthwise separable conv block + self.conv_extra = DepthwiseSeparableConv(base_channels * 4, base_channels * 4) + self.bn_extra = nn.BatchNorm2d(base_channels * 4) if use_batch_norm else nn.Identity() + + # Final conv to reduce channels + self.conv4 = nn.Conv2d(base_channels * 4, base_channels * 2, 1) + self.bn4 = nn.BatchNorm2d(base_channels * 2) if use_batch_norm else nn.Identity() + + self.dropout = nn.Dropout(dropout_rate) + + # Classifier + if use_global_avg_pool: + self.classifier = nn.Linear(base_channels * 2, num_classes) + else: + # Calculate size after pooling + self.classifier = nn.Linear(base_channels * 2 * 3 * 3, num_classes) + + def forward(self, x): + # First conv block + x = F.relu(self.bn1(self.conv1(x))) + x = F.max_pool2d(x, 2) # 28x28 -> 14x14 + + # Depthwise conv blocks + x = F.relu(self.bn2(self.conv2(x))) + x = F.max_pool2d(x, 2) # 14x14 -> 7x7 + + x = F.relu(self.bn3(self.conv3(x))) + x = F.max_pool2d(x, 2) # 7x7 -> 3x3 + + # Extra block + x = F.relu(self.bn_extra(self.conv_extra(x))) + + # Final conv + x = F.relu(self.bn4(self.conv4(x))) + + # Global average pooling or flatten + if self.use_global_avg_pool: + x = F.adaptive_avg_pool2d(x, (1, 1)) + x = x.view(x.size(0), -1) + else: + x = x.view(x.size(0), -1) + + x = self.dropout(x) + x = self.classifier(x) + + return x + + def count_parameters(self): + return sum(p.numel() for p in self.parameters() if p.requires_grad) + + +def create_model(cfg): + model = LightweightCNN( + in_channels=cfg.model.in_channels, + num_classes=cfg.model.num_classes, + base_channels=cfg.model.base_channels, + dropout_rate=cfg.model.dropout_rate, + use_batch_norm=cfg.model.use_batch_norm, + use_global_avg_pool=cfg.model.use_global_avg_pool, + ) + return model + + +if __name__ == "__main__": + model = LightweightCNN() + print(f"Total parameters: {model.count_parameters():,}") + + # Test forward pass + x = torch.randn(1, 1, 28, 28) + output = model(x) + print(f"Output shape: {output.shape}") + + # Detailed parameter count using torchinfo + try: + from torchinfo import summary + summary(model, (1, 1, 28, 28)) + except ImportError: + print("Install torchinfo for detailed model summary: pip install torchinfo") \ No newline at end of file diff --git a/tasks/mnistclass/src/trainer.py b/tasks/mnistclass/src/trainer.py new file mode 100644 index 0000000..f7e0bfc --- /dev/null +++ b/tasks/mnistclass/src/trainer.py @@ -0,0 +1,158 @@ +import os +import torch +import torch.nn as nn +from tqdm import tqdm +import wandb + + + +class Trainer:#define the training logic + + def __init__(self, model, train_loader, test_loader, optimizer, cfg): + self.model = model + self.train_loader = train_loader + self.test_loader = test_loader + self.optimizer = optimizer + self.cfg = cfg + self.device = torch.device(cfg.training.device if torch.cuda.is_available() else "cpu") + + # Move model to device + self.model.to(self.device) + + # Loss function + self.criterion = nn.CrossEntropyLoss() + + # Learning rate scheduler + self.scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1) + + # Create save directory + os.makedirs(cfg.training.save_dir, exist_ok=True) + + def train_epoch(self): + self.model.train() + total_loss = 0 + correct = 0 + total = 0 + + pbar = tqdm(self.train_loader, desc="Training")#progress bar + for batch_idx, (data, target) in enumerate(pbar): + data, target = data.to(self.device), target.to(self.device) + + # Zero gradients + self.optimizer.zero_grad() + + # Forward pass + output = self.model(data) + loss = self.criterion(output, target) + + # Backward pass + loss.backward() + self.optimizer.step() + + # Statistics + total_loss += loss.item() + pred = output.argmax(dim=1, keepdim=True) + correct += pred.eq(target.view_as(pred)).sum().item() + total += target.size(0) + + # Update progress bar + pbar.set_postfix({ + "Loss": f"{loss.item():.4f}", + "Acc": f"{100. * correct / total:.2f}%" + }) + + avg_loss = total_loss / len(self.train_loader) + accuracy = 100. * correct / total + + return avg_loss, accuracy + + def validate(self): + + self.model.eval() + total_loss = 0 + correct = 0 + total = 0 + + with torch.no_grad(): + for data, target in tqdm(self.test_loader, desc="Validation"): + data, target = data.to(self.device), target.to(self.device) + + output = self.model(data) + loss = self.criterion(output, target) + + total_loss += loss.item() + pred = output.argmax(dim=1, keepdim=True) + correct += pred.eq(target.view_as(pred)).sum().item() + total += target.size(0) + + avg_loss = total_loss / len(self.test_loader) + accuracy = 100. * correct / total + + return avg_loss, accuracy + + def train(self): + best_accuracy = 0 + + for epoch in range(self.cfg.training.epochs): + print(f"\nEpoch {epoch + 1}/{self.cfg.training.epochs}") + + # Train + train_loss, train_acc = self.train_epoch() + + # Validate + val_loss, val_acc = self.validate() + + # Step scheduler + self.scheduler.step()#steps learning rate + + # Log to wandb + wandb.log({ + "epoch": epoch + 1, + "train_loss": train_loss, + "train_accuracy": train_acc, + "val_loss": val_loss, + "val_accuracy": val_acc, + "learning_rate": self.optimizer.param_groups[0]["lr"], + }) + + print(f"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.2f}%") + print(f"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.2f}%") + + # Save best model + if val_acc > best_accuracy: + best_accuracy = val_acc + self.save_checkpoint(epoch, val_acc, is_best=True) + print(f"New best accuracy: {best_accuracy:.2f}%") + + # Early stopping if target accuracy reached + if val_acc >= 99: + print(f"Target accuracy reached: {val_acc:.2f}%") + break + + print(f"\nTraining completed. Best accuracy: {best_accuracy:.2f}%") + return best_accuracy + + def save_checkpoint(self, epoch, accuracy, is_best=False): + """Save model checkpoint.""" + checkpoint = { + "epoch": epoch, + "model_state_dict": self.model.state_dict(), + "optimizer_state_dict": self.optimizer.state_dict(), + "accuracy": accuracy, + "config": self.cfg, + } + + # Save checkpoint + checkpoint_path = os.path.join(self.cfg.training.save_dir, "checkpoint.pth") + torch.save(checkpoint, checkpoint_path) + + if is_best: + best_path = os.path.join(self.cfg.training.save_dir, "best_model.pth") + torch.save(checkpoint, best_path) + + def load_checkpoint(self, checkpoint_path): + #load saved model checkpoint + checkpoint = torch.load(checkpoint_path, map_location=self.device) + self.model.load_state_dict(checkpoint["model_state_dict"]) + self.optimizer.load_state_dict(checkpoint["optimizer_state_dict"]) + return checkpoint["epoch"], checkpoint["accuracy"] \ No newline at end of file diff --git a/tasks/mnistclass/src/utils.py b/tasks/mnistclass/src/utils.py new file mode 100644 index 0000000..391a06e --- /dev/null +++ b/tasks/mnistclass/src/utils.py @@ -0,0 +1,70 @@ + + +import random +import numpy as np +import torch +import wandb + + +def set_seed(seed): + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def init_wandb(cfg):#creates login thing on its own + wandb.init( + project=cfg.wandb.project, + entity=cfg.wandb.entity, + tags=cfg.wandb.tags, + config=dict(cfg), + ) + + +def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) + + +def get_device(): + if torch.cuda.is_available(): + return torch.device("cuda") + else: + return torch.device("cpu") + + +def create_optimizer(model, cfg): + + if cfg.optimizer.name == "adam": + return torch.optim.Adam( + model.parameters(), + lr=cfg.optimizer.lr, + weight_decay=cfg.optimizer.weight_decay, + betas=cfg.optimizer.betas, + ) + elif cfg.optimizer.name == "sgd": #dont need it + return torch.optim.SGD( + model.parameters(), + lr=cfg.optimizer.lr, + momentum=cfg.optimizer.momentum, + weight_decay=cfg.optimizer.weight_decay, + ) + else: + raise ValueError(f"Unknown optimizer: {cfg.optimizer.name}") + + +def log_model_info(model): + + param_count = count_parameters(model) + print(f"model created with {param_count:,} parameters") + + if param_count >= 10000: + print("WARNING: model has >= 10,000 parameters!") + + # Log to wandb + wandb.log({"model_parameters": param_count}) + + return param_count \ No newline at end of file diff --git a/tasks/mnistclass/train.py b/tasks/mnistclass/train.py new file mode 100644 index 0000000..c818a77 --- /dev/null +++ b/tasks/mnistclass/train.py @@ -0,0 +1,86 @@ +#main train file + +import hydra +from omegaconf import DictConfig +import torch +import wandb + +from src.model import create_model +from src.dataset import get_dataloaders +from src.trainer import Trainer +from src.utils import set_seed, init_wandb, log_model_info + + +@hydra.main(version_base=None, config_path="configs", config_name="config") +def main(cfg: DictConfig) -> None:#cfg MUST be DictConfig type. And func return NONE + + print("Starting mnist training...") + print(f"Config: {cfg}") + + # Set seed for reproducibility + set_seed(cfg.seed) + + # Initialize wandb + init_wandb(cfg) + + # get dataloaders + train_loader, test_loader = get_dataloaders(cfg) + + + print(f"Train samples: {len(train_loader.dataset)}") + print(f"Test samples: {len(test_loader.dataset)}") + + # Create model + print("creating model...") + model = create_model(cfg) + param_count = log_model_info(model) + + # Verify parameter constraint + if param_count >= 10000:#double check + print(f"ERROR: Model has {param_count:,} parameters (>= 10,000)") + return + + # Create optimizer + optimizer = torch.optim.Adam( + model.parameters(), + lr=cfg.training.learning_rate, + weight_decay=1e-4, + ) + + # Watch model with wandb + wandb.watch(model, log="all") + + # create trainer instance + trainer = Trainer(model, train_loader, test_loader, optimizer, cfg) + + # Start training + print("Starting training...") + best_accuracy = trainer.train() + + # Log final results + print(f"\nTraining completed!") + + + print(f"Best accuracy: {best_accuracy:.2f}%") + print(f"Model parameters: {param_count:,}") + + # Final validation + final_loss, final_acc = trainer.validate() + wandb.log({ + "final_accuracy": final_acc, + "final_loss": final_loss, + "best_accuracy": best_accuracy, + "parameter_count": param_count, + }) + + # Check if target met + if final_acc >= 99: + print("target accuracy achieved!") + else: + print(f"target accuracy not reached. 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a/tasks/prototype/README.md b/tasks/prototype/README.md new file mode 100644 index 0000000..af63227 --- /dev/null +++ b/tasks/prototype/README.md @@ -0,0 +1,101 @@ +# FileNest Local Indexer Prototype + +A concurrent file indexer prototype for the FileNest Framework that processes files locally and stores embeddings in PostgreSQL. + +## Features + +- **Concurrent Processing**: Worker pool with configurable number of goroutines +- **Context Cancellation**: Graceful shutdown with Ctrl+C support +- **Cosine Similarity**: Assigns files to nearest Depth 1 Tagging Vector (D1TV) +- **PostgreSQL Storage**: Efficient storage with conflict resolution +- **Multiple File Types**: Supports .txt, .md, .json, .py, .go, .js, .html +- **Deterministic Embeddings**: Reproducible results for the same content + +## Setup + +1. **Navigate to prototype directory**: +```bash +cd tasks/prototype +``` + +2. **Database Setup**: +```sql +CREATE DATABASE filenest; +CREATE USER filenest_user WITH PASSWORD 'your_password'; +GRANT ALL PRIVILEGES ON DATABASE filenest TO filenest_user; +``` + +3. **Environment Configuration**: +```bash +cp .env.example .env +# Edit .env with your database credentials +``` + +4. **Build and Run**: +```bash +go mod tidy +go build -o prototype cmd/main.go +./prototype index /path/to/your/directory +``` + +## Usage + +```bash +# Index a directory +./prototype index ./test-files + +# With custom path flag +./prototype index -p ./documents ./documents +``` + +## Architecture + +- **Worker Pool**: Configurable number of concurrent workers +- **Job Distribution**: Channel-based job queue with backpressure +- **Result Processing**: Separate goroutine for database operations +- **Timeout Handling**: Per-file processing timeout (default 5s) +- **Graceful Shutdown**: Context cancellation propagated to all goroutines + +## Database Schema + +```sql +CREATE TABLE file_index ( + id SERIAL PRIMARY KEY, + filename TEXT NOT NULL, + filepath TEXT NOT NULL UNIQUE, + embedding FLOAT8[] NOT NULL, + d1tv_id INT NOT NULL, + indexed_at TIMESTAMP DEFAULT now() +); +``` + +## Performance + +- **Concurrent Processing**: 5 workers by default +- **File Size Limit**: 1MB per file +- **Timeout**: 5 seconds per file +- **Connection Pool**: 10 max connections to PostgreSQL + +## Configuration + +Environment variables: +- `DATABASE_URL`: PostgreSQL connection string +- `WORKER_COUNT`: Number of concurrent workers (default: 5) +- `PROCESS_TIMEOUT`: Timeout per file in seconds (default: 5) +- `EMBEDDING_DIM`: Embedding vector dimension (default: 128) + +## Integration with Main Repository + +This prototype is designed to be part of a larger GitHub repository structure: +- Located in `tasks/prototype/` directory +- Self-contained Go module +- Can be run independently or integrated into larger build processes +- Uses proper Go module naming convention for sub-packages + +## Working + +Assigning of D1TV tags concurrently to each of the files +![alt text](image.png) + +Inserted file-D1TV values in Postgres Database +![alt text](image-1.png) \ No newline at end of file diff --git a/tasks/prototype/cmd/main.go b/tasks/prototype/cmd/main.go new file mode 100644 index 0000000..d605547 --- /dev/null +++ b/tasks/prototype/cmd/main.go @@ -0,0 +1,107 @@ +package main + +import ( + "context" + "log" + "os" + "os/signal" + "path/filepath" + "syscall" + + "github.com/centauri1219/FileNest/tasks/prototype/internal/config" + "github.com/centauri1219/FileNest/tasks/prototype/internal/database" + "github.com/centauri1219/FileNest/tasks/prototype/internal/indexer" + + "github.com/spf13/cobra" +) + +var ( + cfg *config.Config // used to access configuration settings throughout program + db *database.Database // connection to PostgreSQL database + rootPath string // stores the directory path to index +) + +// basically sets up the command line interface (CLI) using Cobra to start indexing files +var rootCmd = &cobra.Command{ //main command for CLI + Use: "prototype", + Short: "FileNest Local File Indexer Prototype", + Long: "A concurrent file indexer prototype that processes files and stores embeddings in Postgres", +} + +var indexCmd = &cobra.Command{ // subcommand to index files in a directory + Use: "index [directory]", + Short: "Index files in a directory", + Args: cobra.ExactArgs(1), //exactly one directory argument is required + Run: runIndex, // tells cobra to call the runIndex function +} + +func init() { //setup the CLI commands and flags + rootCmd.AddCommand(indexCmd) // adds the index command as a subcommand of the root command + indexCmd.Flags().StringVarP(&rootPath, "path", "p", "", "Directory path to index") //It allows users to optionally specify a directory path with -p or --path when running the index command. +} + +func main() { + // Load configuration settings like database URL, worker count, etc. + cfg = config.LoadConfig() + + // Setup graceful shutdown by creating a context that can be cancelled + ctx, cancel := context.WithCancel(context.Background()) + defer cancel() + + // Handle interrupt signals + sigChan := make(chan os.Signal, 1) //to listen for OS interrupt like Ctrl+C os.signal is a buffer channel + signal.Notify(sigChan, syscall.SIGINT, syscall.SIGTERM) //registers sigchan to recieve SIGINT + + go func() { // go routine that waits for an interrupt signal + sig := <-sigChan + log.Printf("Received signal %v, shutting down gracefully...", sig) + cancel() // cancel context + }() + + // Initialize database + var err error + db, err = database.NewDatabase(ctx, cfg.DatabaseURL) // connects to the PostgreSQL database using the URL from the config + if err != nil { + log.Fatalf("Failed to initialize: %v", err) + } + defer db.Close() + + log.Println("FileNest Indexer Prototype initialized successfully") + + if err := rootCmd.ExecuteContext(ctx); err != nil { //starts the cobra CLI, passes context for shutdown + log.Fatalf("Command execution failed: %v", err) + } +} + +func runIndex(cmd *cobra.Command, args []string) { + dirPath := args[0] // directory path to index is passed as the first argument + + // Validate directory + if _, err := os.Stat(dirPath); os.IsNotExist(err) { //checks if the directory exists + log.Fatalf("Directory does not exist: %s", dirPath) + } + + absPath, err := filepath.Abs(dirPath) // for consistency + if err != nil { + log.Fatalf("Failed to get absolute path: %v", err) + } + + log.Printf("started indexing of directory: %s", absPath) //start of the indexing process + log.Printf("worker count: %d", cfg.WorkerCount) + log.Printf("process timeout: %d seconds", cfg.ProcessTimeout) + log.Printf("embedding dimension: %d", cfg.EmbeddingDim) //prints out key configuration settings + + // Create indexer + idx := indexer.NewIndexer(db, cfg.WorkerCount, cfg.ProcessTimeout, cfg.EmbeddingDim) //creates new indexer object + + // Start indexing + if err := idx.IndexDirectory(cmd.Context(), absPath); err != nil { // calls Index Directory method to start processing files + if err == context.Canceled { + log.Println("Indexing cancelled") // error if the context was cancelled (like by Ctrl+C) + } else { + log.Fatalf("Indexing failed: %v", err) //any other error + } + } + + log.Println("Indexing completed successfully") +} diff --git a/tasks/prototype/go.mod b/tasks/prototype/go.mod new file mode 100644 index 0000000..4434bfe --- /dev/null +++ b/tasks/prototype/go.mod @@ -0,0 +1,20 @@ +module github.com/centauri1219/FileNest/tasks/prototype + +go 1.21 + +require ( + github.com/jackc/pgx/v5 v5.4.3 + github.com/joho/godotenv v1.5.1 + github.com/spf13/cobra v1.7.0 +) + +require ( + github.com/inconshreveable/mousetrap v1.1.0 // indirect + github.com/jackc/pgpassfile v1.0.0 // indirect + github.com/jackc/pgservicefile v0.0.0-20221227161230-091c0ba34f0a // indirect + github.com/jackc/puddle/v2 v2.2.1 // indirect + github.com/spf13/pflag v1.0.5 // indirect + golang.org/x/crypto v0.9.0 // indirect + golang.org/x/sync v0.1.0 // indirect + golang.org/x/text v0.9.0 // indirect +) diff --git a/tasks/prototype/go.sum b/tasks/prototype/go.sum new file mode 100644 index 0000000..95a0a19 --- /dev/null +++ b/tasks/prototype/go.sum @@ -0,0 +1,38 @@ +github.com/cpuguy83/go-md2man/v2 v2.0.2/go.mod h1:tgQtvFlXSQOSOSIRvRPT7W67SCa46tRHOmNcaadrF8o= +github.com/davecgh/go-spew v1.1.0/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38= +github.com/davecgh/go-spew v1.1.1 h1:vj9j/u1bqnvCEfJOwUhtlOARqs3+rkHYY13jYWTU97c= +github.com/davecgh/go-spew v1.1.1/go.mod h1:J7Y8YcW2NihsgmVo/mv3lAwl/skON4iLHjSsI+c5H38= +github.com/inconshreveable/mousetrap v1.1.0 h1:wN+x4NVGpMsO7ErUn/mUI3vEoE6Jt13X2s0bqwp9tc8= +github.com/inconshreveable/mousetrap v1.1.0/go.mod h1:vpF70FUmC8bwa3OWnCshd2FqLfsEA9PFc4w1p2J65bw= +github.com/jackc/pgpassfile v1.0.0 h1:/6Hmqy13Ss2zCq62VdNG8tM1wchn8zjSGOBJ6icpsIM= +github.com/jackc/pgpassfile v1.0.0/go.mod h1:CEx0iS5ambNFdcRtxPj5JhEz+xB6uRky5eyVu/W2HEg= +github.com/jackc/pgservicefile v0.0.0-20221227161230-091c0ba34f0a 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b/tasks/prototype/internal/config/config.go new file mode 100644 index 0000000..b788a9f --- /dev/null +++ b/tasks/prototype/internal/config/config.go @@ -0,0 +1,46 @@ +package config + +//handles configuration settings for the FileNest prototype, loading them from .env file +import ( + "log" + "os" + "strconv" + + "github.com/joho/godotenv" +) + +type Config struct { + DatabaseURL string + WorkerCount int + ProcessTimeout int // seconds + EmbeddingDim int +} + +func LoadConfig() *Config { + if err := godotenv.Load(); err != nil { // loads environment variables from a .env file if it exists + log.Println("No .env file found, using environment variables") + } + + return &Config{ // pointer to a Config struct + DatabaseURL: getEnv("DATABASE_URL", "postgres://user:password@localhost:5432/filenest?sslmode=disable"), //fetches the env variable or used a default value + WorkerCount: getEnvAsInt("WORKER_COUNT", 5), + ProcessTimeout: getEnvAsInt("PROCESS_TIMEOUT", 5), + EmbeddingDim: getEnvAsInt("EMBEDDING_DIM", 128), + } +} + +func getEnv(key, defaultValue string) string { + if value := os.Getenv(key); value != "" { //checks if the environment variable is set + return value + } + return defaultValue //else returns the default value +} + +func getEnvAsInt(key string, defaultValue int) int { + if value := os.Getenv(key); value != "" { + if intVal, err := strconv.Atoi(value); err == nil { + return intVal + } + } + return defaultValue +} diff --git a/tasks/prototype/internal/database/postgres.go b/tasks/prototype/internal/database/postgres.go new file mode 100644 index 0000000..12e0362 --- /dev/null +++ b/tasks/prototype/internal/database/postgres.go @@ -0,0 +1,116 @@ +package database + +// manages the pooled connection to a PostgreSQL database, initializes the schema, and provides methods to insert and retrieve file index data +import ( + "context" + "fmt" + "time" + + "github.com/centauri1219/FileNest/tasks/prototype/internal/models" + + "github.com/jackc/pgx/v5/pgxpool" +) + +type Database struct { + pool *pgxpool.Pool // connection pool to the PostgreSQL database, pool manages concurrent access +} + +func NewDatabase(ctx context.Context, databaseURL string) (*Database, error) { + config, err := pgxpool.ParseConfig(databaseURL) // parses the dataset URL + if err != nil { + return nil, fmt.Errorf("failed to parse database URL: %w", err) + } + + // Configure connection pool + config.MaxConns = 10 + config.MinConns = 2 + config.MaxConnLifetime = time.Hour //sets how long connection lives + config.MaxConnIdleTime = time.Minute * 30 + + pool, err := pgxpool.NewWithConfig(ctx, config) //initialises conneciton pool + if err != nil { + return nil, fmt.Errorf("failed to create connection pool: %w", err) + } + + // Test connection + if err := pool.Ping(ctx); err != nil { //pings the database to ensure it's reachable + return nil, fmt.Errorf("failed to ping database: %w", err) + } + + db := &Database{pool: pool} //new database instance iwthin the pool + + // Initialize schema + if err := db.initSchema(ctx); err != nil { //to create tavles and indexes if they don't exist + return nil, fmt.Errorf("failed to initialize schema: %w", err) + } + + return db, nil +} + +func (db *Database) Close() { + db.pool.Close() //close all connections in the pool +} + +func (db *Database) initSchema(ctx context.Context) error { //create file_index table + schema := ` + CREATE TABLE IF NOT EXISTS file_index ( + id SERIAL PRIMARY KEY, + filename TEXT NOT NULL, + filepath TEXT NOT NULL UNIQUE, + embedding FLOAT8[] NOT NULL, + d1tv_id INT NOT NULL, + indexed_at TIMESTAMP DEFAULT now() + ); + + CREATE INDEX IF NOT EXISTS idx_file_index_d1tv_id ON file_index(d1tv_id); + CREATE INDEX IF NOT EXISTS idx_file_index_filepath ON file_index(filepath); + ` + + _, err := db.pool.Exec(ctx, schema) //aquires a connection from the pool + return err +} + +func (db *Database) InsertFileIndex(ctx context.Context, fileIndex *models.FileIndex) error { // inserts a new file index or updates an existing one based on the filepath + query := ` + INSERT INTO file_index (filename, filepath, embedding, d1tv_id) + VALUES ($1, $2, $3, $4) + ON CONFLICT (filepath) DO UPDATE SET + filename = EXCLUDED.filename, + embedding = EXCLUDED.embedding, + d1tv_id = EXCLUDED.d1tv_id, + indexed_at = now() + RETURNING id, indexed_at + ` + + err := db.pool.QueryRow( // executes the query and returns the ID and indexed_at timestamp + ctx, // context for cancellation + query, + fileIndex.Filename, + fileIndex.Filepath, + fileIndex.Embedding, + fileIndex.D1TVID, + ).Scan(&fileIndex.ID, &fileIndex.IndexedAt) + + return err +} + +func (db *Database) GetFileIndexStats(ctx context.Context) (map[int]int, error) { + query := `SELECT d1tv_id, COUNT(*) FROM file_index GROUP BY d1tv_id ORDER BY d1tv_id` + // retrieves the count of indexed files grouped by d1tv_id + rows, err := db.pool.Query(ctx, query) + if err != nil { + return nil, err + } + defer rows.Close() + + stats := make(map[int]int) + for rows.Next() { + var d1tvID, count int // key is d1tv_id and value is count of files + if err := rows.Scan(&d1tvID, &count); err != nil { + return nil, err + } + stats[d1tvID] = count + } + + return stats, rows.Err() +} diff --git a/tasks/prototype/internal/indexer/embeddings.go b/tasks/prototype/internal/indexer/embeddings.go new file mode 100644 index 0000000..202523b --- /dev/null +++ b/tasks/prototype/internal/indexer/embeddings.go @@ -0,0 +1,62 @@ +package indexer + +import ( + "math" + "math/rand" +) + +func generateEmbedding(content string, dimension int) []float64 { + var vec []float64 //empty slice to hold the embedding vector + var norm float64 = 0 + for i := 0; i < dimension; i++ { + randNo := rand.Float64() + norm += randNo * randNo //sum of squares for normalization + vec = append(vec, randNo) + } + for i := range vec { + vec[i] = vec[i] / math.Sqrt(norm) + } + return vec +} + +func normalizeVector(vec []float64) []float64 { //normalises vector to unit length + var norm float64 + for _, v := range vec { + norm += v * v + } + norm = math.Sqrt(norm) + + if norm == 0 { + return vec + } + + normalized := make([]float64, len(vec)) + for i, v := range vec { + normalized[i] = v / norm + } + return normalized +} + +// initializeD1TVs creates 10 predefined Depth 1 Tagging Vectors +func initializeD1TVs(dimension int) [][]float64 { + d1tvs := make([][]float64, 10) + + // Create diverse, well-separated centroids + seeds := []int64{ + 1001, 2002, 3003, 4004, 5005, + 6006, 7007, 8008, 9009, 10010, + } //10 unique seeds for random number generation + + for i, seed := range seeds { + rng := rand.New(rand.NewSource(seed)) + d1tv := make([]float64, dimension) //create a new vector of the specified dimension + + for j := range d1tv { + d1tv[j] = rng.NormFloat64() + } + + d1tvs[i] = normalizeVector(d1tv) + } + + return d1tvs +} diff --git a/tasks/prototype/internal/indexer/similarity.go b/tasks/prototype/internal/indexer/similarity.go new file mode 100644 index 0000000..7b79da3 --- /dev/null +++ b/tasks/prototype/internal/indexer/similarity.go @@ -0,0 +1,42 @@ +package indexer + +import "math" + +func cosineSimilarity(a, b []float64) float64 { + if len(a) != len(b) { + return 0 + } + + var dot, norma, normb float64 + + for i := range a { + dot += a[i] * b[i] + norma += a[i] * a[i] + normb += b[i] * b[i] + } + + norma = math.Sqrt(norma) + normb = math.Sqrt(normb) + + if norma == 0 || normb == 0 { + return 0 + } + + return dot / (norma * normb) +} + +// findBestD1TV finds the D1TV with highest cosine similarity to the given embedding +func findBestD1TV(embedding []float64, d1tvs [][]float64) (int, float64) { //each d1tv is []float64 * 10 so basically [][10] float64 + bestd1tvid := 0 + bestd1tvsimilarity := -1.0 + + for i, d1tv := range d1tvs { + similarity := cosineSimilarity(embedding, d1tv) + if similarity > bestd1tvsimilarity { + bestd1tvsimilarity = similarity + bestd1tvid = i + } + } + + return bestd1tvid, bestd1tvsimilarity +} diff --git a/tasks/prototype/internal/indexer/worker.go b/tasks/prototype/internal/indexer/worker.go new file mode 100644 index 0000000..2014d0e --- /dev/null +++ b/tasks/prototype/internal/indexer/worker.go @@ -0,0 +1,237 @@ +package indexer + +import ( + "context" + "io/fs" + "log" + "os" + "path/filepath" + "strings" + "sync" + "time" + + "github.com/centauri1219/FileNest/tasks/prototype/internal/database" + "github.com/centauri1219/FileNest/tasks/prototype/internal/models" +) + +type Indexer struct { //holds the database, d1tv centroids, worker etc + db *database.Database + d1tvs [][]float64 + workerCount int + timeout time.Duration + embeddingDim int +} + +func NewIndexer(db *database.Database, workerCount int, timeoutSeconds int, embeddingDim int) *Indexer { + return &Indexer{ + db: db, + d1tvs: initializeD1TVs(embeddingDim), + workerCount: workerCount, + timeout: time.Duration(timeoutSeconds) * time.Second, + embeddingDim: embeddingDim, + } +} + +func (idx *Indexer) IndexDirectory(ctx context.Context, dirPath string) error { + // Create channels + jobChan := make(chan models.FileJob, idx.workerCount*2) // holds file path and content to be processed + resultChan := make(chan models.ProcessResult, idx.workerCount*2) // holds processed results like embedding + + // Start workers + var wg sync.WaitGroup //tracks when all workers finish + for i := 0; i < idx.workerCount; i++ { + wg.Add(1) // add worker to the wait group + go idx.worker(ctx, i+1, jobChan, resultChan, &wg) + } + + // Start result processor + var processorWg sync.WaitGroup + processorWg.Add(1) + go idx.resultProcessor(ctx, resultChan, &processorWg) //Reads from resultChan and inserts into PostgreSQL + + // Send jobs + jobCount := 0 + err := idx.walkDirectory(ctx, dirPath, jobChan, &jobCount) //sends file jobs to jobChan + close(jobChan) // close job channel when done + + if err != nil { + log.Printf("Error walking directory: %v", err) + } + + // Wait for workers to finish + wg.Wait() + close(resultChan) + + // Wait for result processor to finish + processorWg.Wait() + + log.Printf("Indexing completed. Processed %d files.", jobCount) + return idx.printStats(ctx) +} + +func (idx *Indexer) walkDirectory(ctx context.Context, dirPath string, jobChan chan<- models.FileJob, jobCount *int) error { + supportedExts := map[string]bool{ + ".txt": true, ".md": true, ".json": true, + ".py": true, ".go": true, ".js": true, ".html": true, + } + + return filepath.WalkDir(dirPath, func(path string, d fs.DirEntry, err error) error { //walks the directory tree + if err != nil { + log.Printf("Error accessing path %s: %v", path, err) + return nil // Continue walking + } + + if d.IsDir() { // Skip directories + return nil + } + + ext := strings.ToLower(filepath.Ext(path)) + if !supportedExts[ext] { + return nil + } // if the file extension is not supported, skip it + + // Check context cancellation + select { + case <-ctx.Done(): // if the context is cancelled, return the error + return ctx.Err() + default: + } + + content, err := os.ReadFile(path) + if err != nil { + log.Printf("Error reading file %s: %v", path, err) + return nil + } + + // Skip empty files or files that are too large + if len(content) == 0 || len(content) > 1024*1024 { // 1MB limit + return nil + } + + job := models.FileJob{ + Filepath: path, + Content: string(content), //creates job + } + + select { + case jobChan <- job: //sends the job to the job channel + *jobCount++ + case <-ctx.Done(): + return ctx.Err() + } + + return nil + }) +} + +func (idx *Indexer) worker(ctx context.Context, workerID int, jobChan <-chan models.FileJob, resultChan chan<- models.ProcessResult, wg *sync.WaitGroup) { //go routine fucnction for each worker + defer wg.Done() + + log.Printf("[Worker-%d] Started", workerID) + defer log.Printf("[Worker-%d] Stopped", workerID) + + for { + select { + case job, ok := <-jobChan: + if !ok { + return // Channel closed + } + + result := idx.processFile(ctx, workerID, job) + + select { + case resultChan <- result: + case <-ctx.Done(): + return + } + + case <-ctx.Done(): + return + } + } +} + +func (idx *Indexer) processFile(ctx context.Context, workerID int, job models.FileJob) models.ProcessResult { + // Create timeout context for this file + fileCtx, cancel := context.WithTimeout(ctx, idx.timeout) //timeout for processing + defer cancel() + + result := models.ProcessResult{ + FileJob: job, + } + + // Check for cancellation + select { + case <-fileCtx.Done(): + result.Error = fileCtx.Err() + return result + default: + } // checks if content was cancelled before starting + + // Generate embedding + embedding := generateEmbedding(job.Content, idx.embeddingDim) + result.Embedding = embedding + + // Find best D1TV + d1tvID, similarity := findBestD1TV(embedding, idx.d1tvs) + result.D1TVID = d1tvID + result.Similarity = similarity + + log.Printf("[Worker-%d] Processed file: %s , D1TV: %d (similarity: %.3f)", + workerID, filepath.Base(job.Filepath), d1tvID, similarity) + + return result +} + +func (idx *Indexer) resultProcessor(ctx context.Context, resultChan <-chan models.ProcessResult, wg *sync.WaitGroup) { // this go routine processes results from all the workers and stores them in the database + defer wg.Done() + + log.Println("[ResultProcessor] Started") + defer log.Println("[ResultProcessor] Stopped") + + for { + select { + case result, ok := <-resultChan: + if !ok { + return // Channel closed + } + + if result.Error != nil { + log.Printf("[ResultProcessor] Error processing %s: %v", result.FileJob.Filepath, result.Error) + continue + } + + fileIndex := &models.FileIndex{ + Filename: filepath.Base(result.FileJob.Filepath), + Filepath: result.FileJob.Filepath, + Embedding: result.Embedding, + D1TVID: result.D1TVID, + } + + if err := idx.db.InsertFileIndex(ctx, fileIndex); err != nil { //inserts into db + log.Printf("[ResultProcessor] Error storing %s: %v", result.FileJob.Filepath, err) + } + + case <-ctx.Done(): + return + } + } +} + +func (idx *Indexer) printStats(ctx context.Context) error { + stats, err := idx.db.GetFileIndexStats(ctx) // map of d1tv_id to count of indexed files + if err != nil { + return err + } + + log.Println("\n=== Indexing Statistics ===") + totalFiles := 0 + for d1tvID, count := range stats { + log.Printf("D1TV %d: %d files", d1tvID, count) + totalFiles += count + } + log.Printf("Total indexed files: %d", totalFiles) + log.Println("===========================") + + return nil +} diff --git a/tasks/prototype/test/downloaded_hehe.txt b/tasks/prototype/test/downloaded_hehe.txt new file mode 100644 index 0000000..ab12049 --- /dev/null +++ b/tasks/prototype/test/downloaded_hehe.txt @@ -0,0 +1 @@ +atharv is great \ No newline at end of file diff --git a/tasks/prototype/test/file2.txt b/tasks/prototype/test/file2.txt new file mode 100644 index 0000000..0e2f51c --- /dev/null +++ b/tasks/prototype/test/file2.txt @@ -0,0 +1,2 @@ +my name is khalil + diff --git a/tasks/prototype/test/hello.txt b/tasks/prototype/test/hello.txt new file mode 100644 index 0000000..28f8701 --- /dev/null +++ b/tasks/prototype/test/hello.txt @@ -0,0 +1 @@ +my name is atharv diff --git a/tasks/prototype/test/ijwceucewo.md b/tasks/prototype/test/ijwceucewo.md new file mode 100644 index 0000000..c9acc39 --- /dev/null +++ b/tasks/prototype/test/ijwceucewo.md @@ -0,0 +1 @@ +Starlight \ No newline at end of file diff --git a/tasks/prototype/test/kosihddneih.txt b/tasks/prototype/test/kosihddneih.txt new file mode 100644 index 0000000..a31c36e --- /dev/null +++ b/tasks/prototype/test/kosihddneih.txt @@ -0,0 +1 @@ +ayonakoji kun \ No newline at end of file diff --git a/tasks/prototype/test/kowcwnicew.txt b/tasks/prototype/test/kowcwnicew.txt new file mode 100644 index 0000000..ec7a138 --- /dev/null +++ b/tasks/prototype/test/kowcwnicew.txt @@ -0,0 +1 @@ +LEWIS HAMILTON \ No newline at end of file diff --git a/tasks/prototype/test/lgos.txt b/tasks/prototype/test/lgos.txt new file mode 100644 index 0000000..c6a7265 --- /dev/null +++ b/tasks/prototype/test/lgos.txt @@ -0,0 +1 @@ +atharv e espanol \ No newline at end of file diff --git a/tasks/prototype/test/okiwhefw.txt b/tasks/prototype/test/okiwhefw.txt new file mode 100644 index 0000000..95540ae --- /dev/null +++ b/tasks/prototype/test/okiwhefw.txt @@ -0,0 +1 @@ +Billy Butcher \ No newline at end of file diff --git a/tasks/prototype/test/sfgrgrg.txt b/tasks/prototype/test/sfgrgrg.txt new file mode 100644 index 0000000..5e899fc --- /dev/null +++ b/tasks/prototype/test/sfgrgrg.txt @@ -0,0 +1 @@ +tung utng tung tung tung sahurrrrrrrrrrrrrrrr \ No newline at end of file diff --git a/tasks/prototype/test/sfsf.txt b/tasks/prototype/test/sfsf.txt new file mode 100644 index 0000000..ac15982 --- /dev/null +++ b/tasks/prototype/test/sfsf.txt @@ -0,0 +1 @@ +jem apeele athar \ No newline at end of file diff --git a/tasks/prototype/test/wcjiweuve.txt b/tasks/prototype/test/wcjiweuve.txt new file mode 100644 index 0000000..772bba1 --- /dev/null +++ b/tasks/prototype/test/wcjiweuve.txt @@ -0,0 +1 @@ +Homelander \ No newline at end of file diff --git a/tasks/udpchat/README.md b/tasks/udpchat/README.md new file mode 100644 index 0000000..2cb0f11 --- /dev/null +++ b/tasks/udpchat/README.md @@ -0,0 +1,54 @@ +# UDPChat + +A simple peer-to-peer (P2P) chat application using UDP sockets in Go. Each peer can both send and receive messages using a fixed port, so the sender's port is always visible to the receiver. + +## How It Works +- Each peer runs the same program and listens on a user-specified UDP port. +- You enter the address (IP:port) of the peer you want to chat with. +- Messages are sent and received using the same UDP socket, so the sender's port is always the listening port. + +## Usage + +### 1. Build +``` +go build -o udpchat p2psharing.go +``` + +### 2. Run Two Peers (in separate terminals) + +#### Peer 1 Example +``` +$ ./udpchat +Enter listener port: 8080 +Enter peer address: 127.0.0.1:8081 +You: +``` + +#### Peer 2 Example +``` +$ ./udpchat +Enter listener port: 8081 +Enter peer address: 127.0.0.1:8080 +You: +``` + +## Result +Simulating 2 peer chat on different ports +![alt text](image.png) +- Type messages and press Enter to send. +- Received messages will show the sender's IP and port. + +## Notes +- Use `127.0.0.1` for local testing, or your LAN IP for networked peers. +- Both peers must use different listening ports. +- The sender's port shown in received messages will always match the peer's listening port. + +## Example Output +``` +... +From [127.0.0.1:8081]: Hello! +You: +``` + +## License +MIT diff --git a/tasks/udpchat/go.mod b/tasks/udpchat/go.mod new file mode 100644 index 0000000..df77487 --- /dev/null +++ b/tasks/udpchat/go.mod @@ -0,0 +1,3 @@ +module github.com/centauri1219/FileNest + +go 1.24.4 diff --git a/tasks/udpchat/image.png b/tasks/udpchat/image.png new file mode 100644 index 0000000..588a3f9 Binary files /dev/null and b/tasks/udpchat/image.png differ diff --git a/tasks/udpchat/p2psharing.go b/tasks/udpchat/p2psharing.go new file mode 100644 index 0000000..58653f2 --- /dev/null +++ b/tasks/udpchat/p2psharing.go @@ -0,0 +1,57 @@ +package main + +import ( + "bufio" + "fmt" + "net" + "os" + "strings" +) + +func main() { + reader := bufio.NewReader(os.Stdin) + fmt.Print("Enter listener port: ") + peerport, _ := reader.ReadString('\n') + peerport = strings.TrimSpace(peerport) + + listener, err := net.ListenPacket("udp", ":"+peerport) + if err != nil { + fmt.Println("Error listening ", err) + return + } + defer listener.Close() + + go Startlisten(listener) + + fmt.Print("Enter peer address: ") + peerAddrStr, _ := reader.ReadString('\n') + peerAddrStr = strings.TrimSpace(peerAddrStr) + peerUDPAddr, err := net.ResolveUDPAddr("udp", peerAddrStr) + if err != nil { + fmt.Println("Could not resolve peer address: ", err) + return + } + + for { + fmt.Printf("You: ") + msg, _ := reader.ReadString('\n') + listener.WriteTo([]byte(msg), peerUDPAddr) + } +} + +func Startlisten(listener net.PacketConn) { + buf := make([]byte, 1024) + for { + n, addr, err := listener.ReadFrom(buf) + if err != nil { + fmt.Println("Error reading: ", err) + continue + } + if udpAddr, ok := addr.(*net.UDPAddr); ok { + fmt.Printf("...\nFrom [%s:%d]: %s\n", udpAddr.IP, udpAddr.Port, string(buf[:n])) + } else { + fmt.Printf("...\nFrom [%s]: %s\n", addr.String(), string(buf[:n])) + } + fmt.Printf("You: ") + } +}