app.py

import streamlit as st
from PIL import Image
import tensorflow as tf
import numpy as np

def load_model():
    """Load a pre-trained TensorFlow model for image classification."""
    # Use a TensorFlow Hub model or a local TensorFlow model
    model = tf.keras.applications.MobileNetV2(
        input_shape=(224, 224, 3),
        include_top=True,
        weights="imagenet"
    )
    return model

def predict_disease(image_file):
    """Predicts the class of an image using TensorFlow.
    
    Args:
        image_file: The uploaded image file.
        
    Returns:
        A string representing the predicted class.
    """
    try:
        # Load the model
        model = load_model()
        
        # Process the image
        image = Image.open(image_file).convert("RGB").resize((224, 224))
        image_array = np.array(image) / 255.0
        image_array = np.expand_dims(image_array, axis=0)
        
        # Make prediction
        predictions = model.predict(image_array)
        predicted_class = np.argmax(predictions[0])
        
        # Get the class label from ImageNet (as an example)
        # In a real app, you'd map this to plant diseases
        from tensorflow.keras.applications.mobilenet_v2 import decode_predictions
        _, label, confidence = decode_predictions(predictions, top=1)[0][0]
        
        return f"{label} (confidence: {confidence:.2f})"
    except Exception as e:
        return f"Error: {str(e)}"

def main():
    """Creates the Streamlit app."""
    st.title("Image Classification App")
    st.caption("Note: This is using a general ImageNet model, not a plant disease model")
    
    # Upload an image
    image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
    
    # Predict the class
    if image_file is not None:
        # Display the image
        image = Image.open(image_file)
        st.image(image, caption="Uploaded Image", use_column_width=True)
        
        # Add a prediction button
        if st.button("Classify Image"):
            with st.spinner("Analyzing image..."):
                result = predict_disease(image_file)
            
            # Display the prediction
            if result.startswith("Error"):
                st.error(result)
            else:
                st.success(f"Prediction: {result}")

if __name__ == "__main__":
    main()