Update app.py

app.py

@@ -1,57 +1,8 @@
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from fastapi.responses import JSONResponse
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing import image
-import numpy as np
-import logging
-from PIL import Image
-import io
 
-# Configure logging
-logging.basicConfig(level=logging.DEBUG)
+from fastapi import FastAPI
 
-# Initialize FastAPI app
 app = FastAPI()
 
-# Load your trained model
-model = load_model('model.h5')
-class_names = ['Normal', 'bacteria', 'virus']
-
-def preprocess_image(img, target_size):
-    """Resize and preprocess the image for the model."""
-    if img.mode != "RGB":
-        img = img.convert("RGB")
-    img = img.resize(target_size)
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
-    return img_array
-
-@app.post("/predict")
-async def predict(file: UploadFile = File(...)):
-    if not file:
-        raise HTTPException(status_code=400, detail="No file provided")
-    try:
-        # Read the file's content into a BytesIO object
-        img_bytes = io.BytesIO(await file.read())
-        
-        # Use PIL to open the image
-        img = Image.open(img_bytes)
-        img_array = preprocess_image(img, (224, 224))
-        
-        # Make prediction
-        predictions = model.predict(img_array)
-
-        predicted_class = np.argmax(predictions, axis=1)
-        
-        # Return the prediction
-        predictions = {
-            'class': class_names[predicted_class[0]],
-            'confidence': float(predictions[0][predicted_class[0]])
-        }
-        return JSONResponse(content=predictions)
-    except Exception as e:
-        logging.debug(f"Error processing the file: {str(e)}")
-        raise HTTPException(status_code=500, detail=f"Error processing the file: {str(e)}")
-        
-if __name__ == '__main__':
-    app.run()
+@app.get("/")
+def greet_json():
+    return {"Hello": "World!"}