Update app.py

app.py

@@ -1,7 +1,50 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
 import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import json
 
-# Load the existing model
-model = tf.keras.models.load_model('./plant_disease_detection_saved_model')
+app = FastAPI()
+
+# Load the TensorFlow model
+model = tf.keras.models.load_model('./plant_disease_detection.h5')
+
+# Load categories
+with open('./categories.json') as f:
+    categories = json.load(f)
+
+def preprocess_image(image_bytes):
+    # Convert the image to a NumPy array
+    image = Image.open(io.BytesIO(image_bytes))
+    image = image.resize((224, 224))  # Adjust size as needed
+    image_array = np.array(image) / 255.0  # Normalize to [0, 1]
+    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
+    return image_array
+
+@app.post('/predict')
+async def predict(file: UploadFile = File(...)):
+    if file.content_type.startswith('image/') is False:
+        raise HTTPException(status_code=400, detail='Invalid file type')
+
+    image_bytes = await file.read()
+    image_array = preprocess_image(image_bytes)
+
+    # Make prediction
+    predictions = model.predict(image_array)
+    predicted_class = np.argmax(predictions, axis=1)[0]
+
+    # Map to category names
+    predicted_label = categories.get(str(predicted_class), 'Unknown')
+
+    return JSONResponse(content={
+        'class': predicted_label,
+        'confidence': float(predictions[0][predicted_class])
+    })
+
+
+if __name__ == '__main__':
+    import uvicorn
+    uvicorn.run(app, host='0.0.0.0', port=8080)
 
-# Save it in the SavedModel format
-model.save('./plant_disease_detection_saved_model', save_format='tf')