Delete app.py

app.py

@@ -1,83 +0,0 @@
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-import numpy as np
-import cv2
-import pickle
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.image import img_to_array
-
-app = FastAPI()
-
-print("app run")
-# Load the model and the label binarizer
-model = load_model('cnn_model.h5')
-print("model loaded")
-label_binarizer = pickle.load(open('label_transform.pkl', 'rb'))
-print("labels loaded")
-
-# Function to convert images to array
-def convert_image_to_array(image_dir):
-    try:
-        image = cv2.imdecode(np.frombuffer(image_dir, np.uint8), cv2.IMREAD_COLOR)
-        if image is not None:
-            image = cv2.resize(image, (256, 256))   
-            return img_to_array(image)
-        else:
-            return np.array([])
-    except Exception as e:
-        print(f"Error : {e}")
-        return None
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    try:
-        # Read the file and convert it to an array
-        image_data = await file.read()
-        image_array = convert_image_to_array(image_data)
-
-        if image_array.size == 0:
-            return JSONResponse(content={"error": "Invalid image"}, status_code=400)
-
-        # Normalize the image
-        image_array = np.array(image_array, dtype=np.float16) / 255.0
-
-        # Ensure the image_array has the correct shape (1, 256, 256, 3)
-        image_array = np.expand_dims(image_array, axis=0)
-
-        # Make a prediction
-        prediction = model.predict(image_array)
-        predicted_class = label_binarizer.inverse_transform(prediction)[0]
-
-        return {"prediction": predicted_class}
-    except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
-
-# Add a test GET endpoint to manually trigger the prediction
-@app.get("/test-predict")
-def test_predict():
-    try:
-        image_path = 'crop_image1.jpg'
-        image = cv2.imread(image_path)
-        image_array = cv2.resize(image, (256, 256))
-        image_array = img_to_array(image_array)
-
-        if image_array.size == 0:
-            return JSONResponse(content={"error": "Invalid image"}, status_code=400)
-
-        # Normalize the image
-        image_array = np.array(image_array, dtype=np.float16) / 255.0
-
-        # Ensure the image_array has the correct shape (1, 256, 256, 3)
-        image_array = np.expand_dims(image_array, axis=0)
-
-        # Make a prediction
-        prediction = model.predict(image_array)
-        predicted_class = label_binarizer.inverse_transform(prediction)[0]
-
-        return {"prediction": predicted_class}
-    except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="127.0.0.1", port=8000)