ADDED: transcriber

README.md

@@ -256,6 +256,14 @@ percentage, missing keywords, and profile summary.
   - **Diagnosis:** The diagnostician agent analyzes the input data to provide a preliminary diagnosis with possible conditions.
   - **Treatment Recommendations:** The treatment advisor agent formulates a suitable treatment plan based on the diagnosis and user-provided information.
 
+### 27. Audio Video Transcriber
+
+- **Route:** `/transcriber`
+- **Description:** This API endpoint allows users to upload audio or video files to be transcribed into text using the OpenAI Whisper model.
+- **Feature:**
+  - **File Upload:** Users can upload audio or video files in various formats.
+  - **Transcription:** The API uses the OpenAI Whisper model to transcribe the uploaded file into text.
+
 ## Usage
 
 Each endpoint accepts specific parameters as described in the respective endpoint documentation. Users can make POST requests to these endpoints with the required parameters to perform the desired tasks.

api.py

@@ -44,15 +44,15 @@
 from slowapi import Limiter
 from slowapi.util import get_remote_address
 from slowapi.errors import RateLimitExceeded
-
+from groq import Groq
 
 os.environ["LANGCHAIN_TRACING_V2"]="true"
 os.environ["LANGCHAIN_API_KEY"]=os.getenv("LANGCHAIN_API_KEY")
 os.environ["LANGCHAIN_PROJECT"]="genify"
 os.environ["LANGCHAIN_ENDPOINT"]="https://api.smith.langchain.com"
 
 redis = Redis(host=os.getenv("REDIS_HOST"), port=settings.REDIS_PORT, password=os.getenv("REDIS_PASSWORD"))
-
+client = Groq()
 mongo_client = MongoDB(collection_name=os.getenv("MONGO_COLLECTION_USER"))
 users_collection = mongo_client.collection
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
@@ -67,7 +67,7 @@
 async def rate_limit_exceeded_handler(request:Request,exc: RateLimitExceeded):
     return JSONResponse(
         status_code= status.HTTP_429_TOO_MANY_REQUESTS,
-        content= {"response": "Limit exceeded, please try again after 2 minute !!!!!!"}
+        content= {"response": "Limit exceeded, please try later !!!!!!"}
     )
 
 templates = Jinja2Templates(directory="templates")
@@ -898,6 +898,42 @@ async def run_doc_agent(request:Request,gender: str = Form("Male"),
         return ResponseText(response=report)
     except Exception as e:
         return {"error": str(e)}
+
+@app.post("/transcriber", description=
+          """
+          This route can transcribe audio and video files of any format into text.
+          The transcription process uses the OpenAI Whisper model, which is known for its high accuracy and efficiency.
+
+          The OpenAI Whisper model is a state-of-the-art speech recognition system designed to convert spoken language into written text.
+          It leverages advanced machine learning techniques to achieve high accuracy and robustness across various languages, accents, and audio qualities.
+          The Whisper model is part of OpenAI's efforts to provide powerful tools for natural language understanding and generation.
+          """
+          )
+@limiter.limit("5/15minute")
+
+async def transcribe_audio_video(request: Request, file: UploadFile = File(...)):
+    try:
+        file_contents = await file.read()
+
+        transcription = client.audio.transcriptions.create(
+            file=(file.filename, file_contents),
+            model="whisper-large-v3",
+            prompt="",  # Optiona
+            response_format="json",  # Optional
+            temperature=0,  # Optional
+        )
+        db = MongoDB()
+        payload = {
+            "endpoint": "/transcriber",
+            "transcription": transcription.text
+        }
+        mongo_data = {"Document": payload}
+        result = db.insert_data(mongo_data)
+        print(result)
+
+        return JSONResponse(content={"transcription": transcription.text})
+    except Exception as e:
+        return JSONResponse(content={"error": str(e)}, status_code=500)
 
 if __name__ == '__main__':
     import uvicorn