Update app.py

app.py

@@ -1,50 +1,45 @@
-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
-
-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)
-
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import io
+import json
+
+# 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):
+    # Convert the image to a NumPy array
+    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
+
+def predict(image):
+    image_array = preprocess_image(image)
+
+    # 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 predicted_label, float(predictions[0][predicted_class])
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs=[gr.Label(), gr.Number()],
+    title="Plant Disease Detection",
+    description="Upload an image of a plant leaf to detect if it has any diseases."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()