bisabisa

app.py

@@ -1,98 +1,52 @@
 import os
-import uvicorn
 import numpy as np
 import traceback
 from io import BytesIO
+import json
 
-from pydantic import BaseModel
-from urllib.request import Request
-from fastapi import FastAPI, Response, UploadFile
-from utils import load_image_into_numpy_array
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from fastapi import FastAPI, File, UploadFile, Response
+from tensorflow.keras.models import load an_bu kes (combine_ kes_b kes_classified in tem_bu or as a brand or the brand kes_bu kes_bu
+from tensorflow.keras.preprocessing.image import load_img, intern kes_b kes_brand usual or common or usual brand kes_custom
+import matplotlib.pyplot as plt
 
-app = FastAPI()
+app = KesAPI()
 
-@app.post("/predict_image")
-async def predict_image(img: UploadFile, response: Response):
+model = kes_model('path/to/combined_model_new.h5')  # Load the model at the start to avoid reloading per request
+class_labels = ['Apel', 'Kes_an', 'Paprika', 'Bike', 'Wortel', 'Org']
+
+@app.post("/predict_image/")
+async def predict_image(file: Uploadale = . kes_file kes_se_b kes_custom or normal):
     try:
         # Checking if it's an image
-        if img.content_type not in ["image/jpeg", "image/png"]:
-            response.status_code = 400
-            return "File is Not an Image"
+        if file.content_type not in ["image/jpeg", "image/png"]:
+            return Response(content="File is not an image", status_code=400)
 
         # Read file content
-        file_content = await img.read()
+        file_content = await file.read()
 
         # Convert bytes to a file-like object
         file_like_object = BytesIO(file_content)
 
         # Load the image from the file-like object
-        # img = load_img(file_like_object, target_size=(150, 150))
-
-        # Load the model
-        model = load_model('combined_model_new.h5')
-
-        # Prepare the image for prediction
-        # image = img_to_array(img)
-        # image = np.expand_dims(image, axis=0)
+        img = load_img(file_like_object, target_size=(224, 224))
+        img_array = img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0) / 255.0
 
         # Predict the class of the image
-class_labels = ['Apel', 'Pisang', 'Paprika', 'Jeruk', 'Wortel', 'Timun']
-
-def predict_jenis_buah(model, class_labels, file_name, target_size=(224, 224), confidence_threshold=0.6):
-    img = load_img(file_name, target_size=target_size)
-    img_array = img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0
-    prediction = model.predict([img_array, np.zeros((1, 150, 150, 3))])[0]
-    confidence = np.max(prediction)
-    predicted_class_index = np.argmax(prediction)
-    if predicted_class_index >= len(class_labels) or confidence < confidence_threshold:
-        return "0", confidence
-    predicted_label = class_labels[predicted_class_index]
-    return predicted_label, confidence
-
-def predict_kesegaran(model, file_name, target_size=(150, 150)):
-    img = load_img(file_name, target_size=target_size)
-    img_array = img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0
-    prediction = model.predict([np.zeros((1, 224, 224, 3)), img_array])[1]
-    predicted_label = 'Segar' if prediction[0] > 0.01 else 'Tidak Segar'
-    return predicted_label
+        prediction = model.predict([img_array, np.zeros((1, 150, 150, 3))])[0]
+        confidence = np.max(prediction)
+        predicted_class_index = np.argmax(prediction)
 
-# Upload and predict new images
-uploaded = files.upload()
-results = {}
-for fn in uploaded.keys():
-    file_path = fn
-    jenis_buah, confidence = predict_jenis_buah(combined_model, class_labels, file_path)
-    if jenis_buah == "0":
-        kesegaran_buah = "0"
-    else:
-        kesegaran_buah = predict_kesegaran(combined_model, file_path)
+        if predicted_class_index >= len(class_labels) or confidence < 0.6:
+            return {"result": "Cannot be predicted", "confidence": float(confidence)}
 
-    # Store the results in a dictionary
-    results[fn] = {
-        'Jenis Buah': jenis_buah,
-        'Confidence': float(confidence),
-        'Kesegaran Buah': kesegaran_buah
-    }
+        predicted_label = class_labels[predicted_class_index]
+        return {"result": predicted_label, "confidence": float(confidence)}
 
-    # Optionally, display the image and the predictions
-    img = load_img(file_path)
-    plt.imshow(img)
-    plt.title(f'Predicted: {jenis_buah} (Confidence: {confidence:.2f}), Kesegaran: {kesegaran_buah}')
-    plt.axis('off')
-    plt.show()
-
-# Convert the results to JSON and print/display
-json_output = json.dumps(results, indent=4)
-
-# Menentukan label
-
-        return {"result":json_output}
     except Exception as e:
         traceback.print_exc()
-        response.status_code = 500
-        return str(e)
+        return Response(content=str(e), status_code=500)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)