Update app.py

app.py

@@ -22,8 +22,8 @@ def predict_labels_and_probabilities(image_path):
     step_size = 100  # Step size for the grid
     predictions = []
     
-    for y in range(0, img.shape[0] - img_height, step_size):
-        for x in range(0, img.shape[1] - img_width, step_size):
+    for y in range(0, img_rgb.shape[0] - img_height, step_size):
+        for x in range(0, img_rgb.shape[1] - img_width, step_size):
             patch = img_rgb[y:y+img_height, x:x+img_width]
             patch_resized = cv2.resize(patch, (img_height, img_width))
             patch_array = image.img_to_array(patch_resized)
@@ -43,8 +43,10 @@ def predict_labels_and_probabilities(image_path):
             
             if probability > 0.5:  # Threshold to filter out low confidence predictions
                 predictions.append((predicted_class, probability, x, y, x+img_width, y+img_height))
+                # Draw rectangle around detected ingredients
+                cv2.rectangle(img_rgb, (x, y), (x+img_width, y+img_height), (255, 0, 0), 2)
     
-    return predictions
+    return predictions, img_rgb
 
 # Streamlit App
 st.title("Intelligent Recipe Finder Classification")
@@ -56,7 +58,7 @@ if uploaded_file is not None:
     st.image(uploaded_file, caption='Uploaded Ingredient Image.', use_column_width=True)
     
     # Perform the prediction and display the results
-    predictions = predict_labels_and_probabilities(uploaded_file)
+    predictions, img_with_boxes = predict_labels_and_probabilities(uploaded_file)
     st.write("Predictions for detected ingredients in the image:")
     
     for i, (label, probability, x1, y1, x2, y2) in enumerate(predictions):
@@ -65,8 +67,5 @@ if uploaded_file is not None:
         st.write(f"Probability: {probability}")
         st.write(f"Location: ({x1}, {y1}) to ({x2}, {y2})")
         
-        # Draw rectangle around detected ingredients
-        cv2.rectangle(img_rgb, (x1, y1), (x2, y2), (255, 0, 0), 2)
-    
     # Display the image with rectangles
-    st.image(img_rgb, caption='Detected Ingredients', use_column_width=True)
+    st.image(img_with_boxes, caption='Detected Ingredients', use_column_width=True)