Update app.py

app.py

@@ -1,44 +1,59 @@
-from fastapi import FastAPI, UploadFile, HTTPException, Response
-from fastapi.encoders import jsonable_encoder
+from fastapi import FastAPI, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+import numpy as np
+import json
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
-import numpy as np
 from io import BytesIO
-import json
 
 app = FastAPI()
-model = load_model('combined_model_new.h5')
+
+# Load the model
+
+combined_model = load_model('combined_model_new.h5')
 class_labels = ['Apel', 'Pisang', 'Paprika', 'Jeruk', 'Wortel', 'Timun']
 
+def predict_jenis_buah(model, img_array, class_labels, confidence_threshold=0.6):
+    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, img_array):
+    prediction = model.predict([np.zeros((1, 224, 224, 3)), img_array])[1]
+    return 'Segar' if prediction[0] > 0.01 else 'Tidak Segar'
+
 @app.post("/predict_image")
-async def predict_image(img: UploadFile, response: Response):
-    try:
-        if img.content_type not in ["image/jpeg", "image/png"]:
-            raise HTTPException(status_code=400, detail="File is not an image")
-
-        # Read file content and prepare image
-        file_content = await img.read()
-        file_like_object = BytesIO(file_content)
-        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
-
-        # Prediction
-        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 < 0.6:
-            result = {"result": "Cannot be predicted", "confidence": int(confidence*100), }
-        else:
-            predicted_label = class_labels[predicted_class_index]
-            result = {"result": predicted_label, "confidence": int(confidence*100),}
-
-        # Convert result using jsonable_encoder to ensure JSON compatibility
-        return jsonable_encoder(result)
-
-    except Exception as e:
-        traceback.print_exc()
-        return Response(content=str(e), status_code=500)
+async def predict_image(file: UploadFile):
+    if file.content_type not in ["image/jpeg", "image/png"]:
+        raise HTTPException(status_code=400, detail="Unsupported file format")
+
+    # Read image file and prepare it
+    contents = await file.read()
+    img = load_img(BytesIO(contents), target_size=(224, 224))
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0) / 255.0
+
+    # Predict jenis buah
+    jenis_buah, confidence = predict_jenis_buah(combined_model, img_array, class_labels)
+    if jenis_buah == "0":
+        kesegaran_buah = "0"
+    else:
+        img = load_img(BytesIO(contents), target_size=(150, 150))
+        img_array_freshness = img_to_array(img)
+        img_array_freshness = np.expand_dims(img_array_freshness, axis=0) / 255.0
+        kesegaran_buah = predict_kesegaran(combined_model, img_array_freshness)
+
+    # Construct result
+    result = {
+        'Jenis Buah': jenis_buah,
+        'Confidence': int(confidence*100),
+        'Kesegaran Buah': kesegaran_buah
+    }
+
+    return JSONResponse(content=result)