Update app.py

app.py

@@ -4,7 +4,7 @@ import torch.nn as nn
 from torchvision import transforms, models
 from PIL import Image
 import torch.nn.functional as F
-import gradio as gr
+import streamlit as st
 
 class TomatoLeafDiseaseDetectionApp:
     def __init__(self):
@@ -42,12 +42,12 @@ class TomatoLeafDiseaseDetectionApp:
         model.eval()  # Set the model to evaluation mode
         return model
 
-    def predict_disease(self, image_path):
+    def predict_disease(self, image):
         """
         Predict the tomato leaf disease from the given image.
 
         Args:
-            image_path (str): Path to the input image.
+            image (PIL.Image): Input image.
 
         Returns:
             tuple: Predicted disease name and confidence score.
@@ -59,7 +59,6 @@ class TomatoLeafDiseaseDetectionApp:
                 transforms.ToTensor(),
                 transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  # Normalize for EfficientNet
             ])
-            image = Image.open(image_path).convert("RGB")
             input_tensor = transform(image).unsqueeze(0).to(self.device)
 
             # Perform prediction
@@ -74,44 +73,23 @@ class TomatoLeafDiseaseDetectionApp:
         except Exception as e:
             return f"Error: {str(e)}", 0.0
 
-    def gradio_interface(self):
-        """
-        Launch the Gradio interface for tomato leaf disease detection.
-        """
-        def classify_image(image_path):
-            disease_name, confidence = self.predict_disease(image_path)
-            return disease_name, f"Confidence: {confidence:.2f}"
+def main():
+    st.title("Tomato Leaf Disease Detection")
 
-        with gr.Blocks(theme=gr.themes.Soft()) as demo:
-            gr.HTML("<center><h1>Tomato Leaf Disease Detection</h1></center>")
-            
-            with gr.Row():
-                input_image = gr.Image(type="filepath", label="Upload Leaf Image")
-                with gr.Column():
-                    output_label = gr.Label(label="Predicted Disease")
-                    confidence_text = gr.Textbox(label="Confidence Score")
-            
-            with gr.Row():
-                button = gr.Button(value="Detect Disease")
-            
-            button.click(
-                classify_image,
-                inputs=[input_image],
-                outputs=[output_label, confidence_text]
-            )
+    # Upload image
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+    if uploaded_file is not None:
+        # Open the image
+        image = Image.open(uploaded_file).convert("RGB")
+        st.image(image, caption='Uploaded Image.', use_column_width=True)
 
-            gr.Examples(
-                examples=[
-                    "tomato_earlt_blight.jpg",  # Replace with your example paths
-                    "yellow_leaf_curl.jpg",
-                ],
-                inputs=[input_image],
-                outputs=[output_label, confidence_text],
-                label="Example Images"
-            )
+        # Initialize the app
+        app = TomatoLeafDiseaseDetectionApp()
 
-            demo.launch(debug=True)
+        # Predict disease
+        disease_name, confidence = app.predict_disease(image)
+        st.write(f"Predicted Disease: {disease_name}")
+        st.write(f"Confidence Score: {confidence:.2f}")
 
 if __name__ == "__main__":
-    app = TomatoLeafDiseaseDetectionApp()
-    app.gradio_interface()
+    main()