Update app.py

app.py

@@ -19,57 +19,21 @@ def predict(img):
     prediction_label = class_name[pred]
     prediction_status = status[pred]
     recommendation = recommend[pred]
-    
-    # If the predicted label is 1, draw the bounding boxes on the image
-    if pred == 1:
-        # Example: Assuming the model provides bounding box coordinates in a list of tuples
-        # Each tuple is (x, y, w, h) for the bounding box
-        bounding_boxes = [(50, 50, 100, 100), (150, 150, 50, 50)]  # Example coordinates
-        
-        # Create a copy of the original image
-        img_with_boxes = img.copy()
-        
-        # Scale factor to adjust bounding box coordinates
-        scale_x = img.shape[1] / 256
-        scale_y = img.shape[0] / 256
-        
-        # Calculate the merged bounding box
-        min_x = min([x for (x, y, w, h) in bounding_boxes])
-        min_y = min([y for (x, y, w, h) in bounding_boxes])
-        max_x = max([x + w for (x, y, w, h) in bounding_boxes])
-        max_y = max([y + h for (x, y, w, h) in bounding_boxes])
-        
-        # Scale the merged bounding box coordinates back to the original image size
-        min_x_scaled = int(min_x * scale_x)
-        min_y_scaled = int(min_y * scale_y)
-        max_x_scaled = int(max_x * scale_x)
-        max_y_scaled = int(max_y * scale_y)
-        
-        # Draw the merged bounding box on the original image
-        cv2.rectangle(img_with_boxes, (min_x_scaled, min_y_scaled), (max_x_scaled, max_y_scaled), (0, 255, 0), 2)
-    else:
-        img_with_boxes = img
-    
-    # Save the output image
-    output_path = 'output_image.jpg'
-    cv2.imwrite(output_path, img_with_boxes)
-
-    return img_with_boxes, prediction_label, prediction_status, recommendation, output_path
+    return prediction_label, prediction_status, recommendation
 
 
 interface = gr.Interface(
     fn=predict,
     inputs='image',
     outputs=[
-        'image',
         gr.components.Textbox(label='Disease Name'),
         gr.components.Textbox(label='Disease status'),
-        gr.components.Textbox(label='Disease medicine'),
-        gr.components.File(label='Download Image')
+        gr.components.Textbox(label='Disease medicine')
     ],
     examples=[
         ['download (1).jpeg'], ['download (2).jpeg'], ['download (3).jpeg'],
         ['images (1).jpeg'], ['images (2).jpeg'], ['images (3).jpeg']
-    ]
+    ],
+    description="Upload an image of a chicken to predict if it has fowl pox. You will receive a status report and a recommended treatment."
 )
 interface.launch(debug=True)