Update app.py

app.py

@@ -1,112 +1,73 @@
 import streamlit as st
 from PIL import Image
-import torch
-from torchvision import transforms
-import torchvision.models as models
-import requests
-import os
-import json
+import tensorflow as tf
+import numpy as np
 
 def load_model():
-    """Load a pre-trained MobileNetV2 model for plant disease classification."""
-    # Create model directory if it doesn't exist
-    if not os.path.exists("model"):
-        os.makedirs("model")
-    
-    # Download model if not already downloaded
-    model_path = "model/plant_disease_model.pth"
-    if not os.path.exists(model_path):
-        st.info("Downloading model for the first time. This might take a moment...")
-        # Replace this URL with the actual model URL if you have one
-        # For now, we'll use a pre-trained MobileNetV2 and fine-tune it
-        model = models.mobilenet_v2(pretrained=True)
-        num_classes = 38  # Example number of plant disease classes
-        model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, num_classes)
-        torch.save(model.state_dict(), model_path)
-    
-    # Load the model
-    model = models.mobilenet_v2(pretrained=False)
-    num_classes = 38  # Same number as above
-    model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, num_classes)
-    model.load_state_dict(torch.load(model_path))
-    model.eval()
-    
+    """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 get_class_names():
-    """Get the class names for the plant disease model."""
-    # Define a mapping of class indices to disease names
-    # This is a placeholder - replace with your actual class mapping
-    class_names = {
-        0: "Apple___Apple_scab",
-        1: "Apple___Black_rot",
-        # ... add all your classes here
-        37: "Tomato___healthy"
-    }
-    return class_names
-
 def predict_disease(image_file):
-    """Predicts the disease of a plant from an image using PyTorch.
+    """Predicts the class of an image using TensorFlow.
     
     Args:
         image_file: The uploaded image file.
         
     Returns:
-        A string representing the predicted disease.
+        A string representing the predicted class.
     """
     try:
         # Load the model
         model = load_model()
         
-        # Define image transformations
-        transform = transforms.Compose([
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-        ])
-        
         # Process the image
-        image = Image.open(image_file).convert("RGB")
-        image_tensor = transform(image).unsqueeze(0)
+        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
-        with torch.no_grad():
-            outputs = model(image_tensor)
-            _, predicted = torch.max(outputs, 1)
-            predicted_idx = predicted.item()
+        predictions = model.predict(image_array)
+        predicted_class = np.argmax(predictions[0])
         
-        # Get class names
-        class_names = get_class_names()
-        predicted_label = class_names[predicted_idx]
+        # 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 predicted_label
+        return f"{label} (confidence: {confidence:.2f})"
     except Exception as e:
         return f"Error: {str(e)}"
 
 def main():
     """Creates the Streamlit app."""
-    st.title("Plant Disease Detection 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 of a plant", type=["jpg", "jpeg", "png"])
+    image_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
     
-    # Predict the disease
+    # Predict the class
     if image_file is not None:
         # Display the image
         image = Image.open(image_file)
-        st.image(image, caption="Uploaded Plant Image", use_column_width=True)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
         
         # Add a prediction button
-        if st.button("Detect Disease"):
+        if st.button("Classify Image"):
             with st.spinner("Analyzing image..."):
-                disease = predict_disease(image_file)
+                result = predict_disease(image_file)
             
             # Display the prediction
-            if disease.startswith("Error"):
-                st.error(disease)
+            if result.startswith("Error"):
+                st.error(result)
             else:
-                st.success(f"Predicted disease: {disease}")
+                st.success(f"Prediction: {result}")
 
 if __name__ == "__main__":
     main()