Update app.py

app.py

@@ -1,81 +1,38 @@
 import gradio as gr
-from ultralytics import YOLO
+from transformers import pipeline
 from PIL import Image
-import numpy as np
-import cv2
 
-# Load the YOLOv8 model (ensure the model file is correct and accessible)
-try:
-    model = YOLO('yolov8n.pt')
-    print("Model loaded successfully.")
-except Exception as e:
-    print(f"Error loading model: {e}")
+# Load a pre-trained model for plant disease classification
+model = pipeline("image-classification", model="microsoft/resnet-50")  # Substitute with a specific plant disease model if available on Hugging Face
 
-def identify_disease(image):
-    # Convert the image to RGB if it's not already in RGB
-    try:
-        if image.mode != 'RGB':
-            image = image.convert('RGB')
-        print("Image converted to RGB successfully.")
-    except Exception as e:
-        print(f"Error converting image to RGB: {e}")
-        return image, [{"Disease": "Error", "Confidence": "N/A"}]
+def classify_disease(image):
+    # Run the model on the image
+    results = model(image)
     
-    # Perform inference
-    try:
-        results = model(image)
-        predictions = results[0]
-        print("Model inference completed successfully.")
-    except Exception as e:
-        print(f"Error during model inference: {e}")
-        return image, [{"Disease": "Error during inference", "Confidence": "N/A"}]
+    # Format the top result (assuming the top-1 result is most accurate)
+    disease_name = results[0]['label']
+    confidence_score = results[0]['score']
     
-    # Check for detections
-    if len(predictions.boxes) == 0:
-        print("No detections found.")
-        annotated_image = np.array(image)
-        cv2.putText(annotated_image, "No disease detected", (10, 30), 
-                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)
-        annotated_image = Image.fromarray(annotated_image)
-        return annotated_image, [{"Disease": "None", "Confidence": "N/A"}]
-    
-    # Extract predictions and annotate image
-    try:
-        boxes = predictions.boxes
-        labels = boxes.cls.cpu().numpy() if boxes.cls is not None else []
-        scores = boxes.conf.cpu().numpy() if boxes.conf is not None else []
-        class_names = model.names
-        
-        annotated_image = np.array(image)
-        for box, label, score in zip(boxes.xyxy.cpu().numpy(), labels, scores):
-            x1, y1, x2, y2 = map(int, box)
-            class_name = class_names[int(label)]
-            confidence = f"{score * 100:.2f}%"
-            annotated_image = cv2.rectangle(annotated_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            annotated_image = cv2.putText(annotated_image, f"{class_name} {confidence}", (x1, y1 - 10),
-                                          cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-        annotated_image = Image.fromarray(annotated_image)
-        print("Image annotation completed.")
-        
-        # Prepare results list for output
-        results_list = [{"Disease": class_names[int(label)], "Confidence": f"{score * 100:.2f}%"} for label, score in zip(labels, scores)]
-        return annotated_image, results_list
-    
-    except Exception as e:
-        print(f"Error during annotation: {e}")
-        return image, [{"Disease": "Error during annotation", "Confidence": "N/A"}]
+    # Format the output
+    return {
+        "Disease Name": disease_name,
+        "Confidence Score": f"{confidence_score:.2f}",
+        "Uploaded Image": image
+    }
 
-# Define Gradio interface with updated syntax
+# Create Gradio Interface
 interface = gr.Interface(
-    fn=identify_disease,
-    inputs=gr.Image(type="pil"),
+    fn=classify_disease,
+    inputs=gr.inputs.Image(type="pil"),
     outputs=[
-        gr.Image(type="pil", label="Annotated Image"),
-        gr.Dataframe(headers=["Disease", "Confidence"], label="Predictions")
+        gr.outputs.Textbox(label="Disease Name"),
+        gr.outputs.Textbox(label="Confidence Score"),
+        gr.outputs.Image(type="pil", label="Uploaded Image")
     ],
-    title="Leaf Disease Identifier with YOLOv8",
-    description="Upload an image of a leaf, and this tool will identify the disease with confidence scores."
+    title="Tobacco Plant Disease Identification",
+    description="Upload an image of a tobacco plant leaf, and this model will identify the disease and show the confidence score."
 )
 
 # Launch the app
-interface.launch()
+if __name__ == "__main__":
+    interface.launch()