Update app.py

app.py

@@ -1,8 +1,9 @@
+
 import streamlit as st
 import numpy as np
-import cv2
-from tensorflow.keras.preprocessing import image
 import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import cv2
 
 # Load the saved model
 model_path = "fahrnphi_exam_project.keras"
@@ -11,63 +12,58 @@ model = tf.keras.models.load_model(model_path)
 # Set image dimensions
 img_height, img_width = 150, 150  # Input size for the model
 
-# Define class labels
-class_labels = {0: 'Bell Pepper', 1: 'Carrot', 2: 'Garlic', 3: 'Ginger', 4: 'Jalapeno', 5: 'Onion', 6: 'Potato', 7: 'Sweetpotato', 8: 'Tomato'}
-
-# Define a function to predict labels for multiple regions in an image
+# Define a function for prediction and returning labels and probabilities
 def predict_labels_and_probabilities(image_path):
-    # Load the image
-    img = image.load_img(image_path)
-    img_array = image.img_to_array(img)
-
-    # Convert to grayscale
-    gray = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)
-
-    # Apply GaussianBlur to reduce noise and improve contour detection
-    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
-
-    # Find edges in the image using Canny edge detection
-    edged = cv2.Canny(blurred, 50, 150)
-
-    # Find contours in the image
-    contours, _ = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
+    # Load the image using OpenCV
+    img = cv2.imdecode(np.frombuffer(image_path.read(), np.uint8), cv2.IMREAD_COLOR)
+    
+    # Assuming the input image might contain multiple ingredients, we will process it in patches.
+    # For simplicity, let's divide the image into 4 patches and classify each one
+    h, w, _ = img.shape
+    patches = [
+        img[0:h//2, 0:w//2],  # Top-left
+        img[0:h//2, w//2:w],  # Top-right
+        img[h//2:h, 0:w//2],  # Bottom-left
+        img[h//2:h, w//2:w],  # Bottom-right
+    ]
+    
     predictions = []
-
-    for contour in contours:
-        # Create a bounding box around each contour
-        x, y, w, h = cv2.boundingRect(contour)
-        if w > 50 and h > 50:  # Filter out small boxes
-            roi = img_array[y:y+h, x:x+w]
-            roi = cv2.resize(roi, (img_height, img_width))
-            roi = np.expand_dims(roi, axis=0)
-            roi = roi / 255.0  # Scale image pixels
-
-            # Predict with the model
-            preds = model.predict(roi)
-            class_idx = np.argmax(preds[0])
-            predicted_class = class_labels[class_idx]
-            probability = preds[0][class_idx]
-
-            predictions.append((predicted_class, probability))
-
+    
+    for patch in patches:
+        patch_resized = cv2.resize(patch, (img_height, img_width))
+        patch_array = image.img_to_array(patch_resized)
+        patch_array = np.expand_dims(patch_array, axis=0)
+        patch_array /= 255.  # Scale pixel values
+        
+        preds = model.predict(patch_array)
+        class_idx = np.argmax(preds[0])
+        
+        # Map class indices to class names
+        class_labels = {
+            0: 'Bell Pepper', 1: 'Carrot', 2: 'Garlic', 3: 'Ginger',
+            4: 'Jalapeno', 5: 'Onion', 6: 'Potato', 7: 'Sweetpotato', 8: 'Tomato'
+        }
+        predicted_class = class_labels[class_idx]
+        probability = preds[0][class_idx]
+        
+        predictions.append((predicted_class, probability))
+    
     return predictions
 
 # Streamlit App
 st.title("Intelligent Recipe Finder Classification")
 
-uploaded_file = st.file_uploader("Choose an image with ingredients...", type="jpg")
+uploaded_file = st.file_uploader("Choose an ingredients image...", type="jpg")
 
 if uploaded_file is not None:
     # Display the uploaded image
     st.image(uploaded_file, caption='Uploaded Ingredient Image.', use_column_width=True)
 
-    # Predict labels and probabilities for multiple regions
+    # Perform the prediction and display the results
     predictions = predict_labels_and_probabilities(uploaded_file)
-
-    if predictions:
-        st.write("Predictions:")
-        for i, (label, probability) in enumerate(predictions):
-            st.write(f"{i+1}. **Prediction:** {label} | **Probability:** {probability:.2f}")
-    else:
-        st.write("No significant ingredients detected.")
+    st.write("Predictions for different patches of the image:")
+    
+    for i, (label, probability) in enumerate(predictions):
+        st.write(f"Patch {i+1}:")
+        st.write("Prediction:", label)
+        st.write("Probability:", probability)