Update app.py

app.py

@@ -24,23 +24,20 @@ class_names = {
 }
 
 # Helper function: Extract expiry dates
-# Helper function: Extract unique expiry dates
 def extract_expiry_dates(text):
     patterns = [
-      r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})',       # 20/07/2024 or 20-07-2024
-    r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})',       # 20/07/24 or 20-07-24
-    r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})',       # 20 MAY 2024
-    r'([A-Za-z]{3,}\s*\d{1,2}[,\s]*\d{4})',    # July 20, 2024
-    r'(\d{4}[\/\-]\d{1,2}[\/\-]\d{1,2})',       # 2024/07/20 or 2024-07-20
-    r'([A-Za-z]{3}[\-]\d{1,2}[\-]\d{4})'       # JAN-15-2024
+        r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})',       # 20/07/2024 or 20-07-2024
+        r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})',       # 20/07/24 or 20-07-24
+        r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})',       # 20 MAY 2024
+        r'([A-Za-z]{3,}\s*\d{1,2}[,\s]*\d{4})',    # July 20, 2024
+        r'(\d{4}[\/\-]\d{1,2}[\/\-]\d{1,2})',       # 2024/07/20 or 2024-07-20
+        r'([A-Za-z]{3}[\-]\d{1,2}[\-]\d{4})'       # JAN-15-2024
     ]
-    # Helper function: Extract expiry dates
     dates = []
     for pattern in patterns:
         matches = re.findall(pattern, text)
         dates.extend(matches)
 
-    # Deduplicate and prioritize the longest (likely full year) date format
     unique_dates = sorted(dates, key=lambda x: len(x), reverse=True)
     return [unique_dates[0]] if unique_dates else []  # Return the most likely date
 
@@ -58,7 +55,6 @@ def calculate_days_to_expiry(expiry_dates):
     today = datetime.now()
     for date_str in expiry_dates:
         try:
-            # Parse date
             if '/' in date_str or '-' in date_str:
                 if len(date_str.split('/')[-1]) == 2 or len(date_str.split('-')[-1]) == 2:
                     date_obj = datetime.strptime(date_str, '%d/%m/%y') if '/' in date_str else datetime.strptime(date_str, '%d-%m-%y')
@@ -67,7 +63,6 @@ def calculate_days_to_expiry(expiry_dates):
             else:
                 date_obj = datetime.strptime(date_str, '%d %B %Y')
 
-            # Calculate difference
             delta = (date_obj - today).days
             if delta >= 0:
                 results.append(f"{date_str}: {delta} days to expire")
@@ -77,13 +72,18 @@ def calculate_days_to_expiry(expiry_dates):
             results.append(f"{date_str}: Invalid date format")
     return results
 
+# Initialize session state
+if 'uploaded_file' not in st.session_state:
+    st.session_state['uploaded_file'] = None
+
 # Streamlit App
 st.title("Flipkart Grid")
 
 # Sidebar options
 app_mode = st.sidebar.selectbox(
     "Choose the mode",
-    ["Home", "Brand & Text Detection", "Fruit Freshness Detection", "Object Detection"]
+    ["Home", "Brand & Text Detection", "Fruit Freshness Detection", "Object Detection"],
+    on_change=lambda: st.session_state.update({'uploaded_file': None})  # Reset uploaded file on mode change
 )
 
 if app_mode == "Home":
@@ -95,113 +95,103 @@ if app_mode == "Home":
     - *Object Detection*: Detect objects in uploaded images.
     """)
 
-elif app_mode == "Brand & Text Detection":
-    st.header("Brand & Text Detection")
-    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
-
-        # Brand detection
-        results = brand_model.predict(source=image, stream=False)
-        detected_image = results[0].plot()
-
-        # OCR for text extraction
-        _, img_buffer = cv2.imencode('.jpg', image)
-        ocr_result = ocr.ocr(img_buffer.tobytes())
-        if ocr_result and isinstance(ocr_result[0], list) and len(ocr_result[0]) > 0:
-            extracted_text = ' '.join([line[1][0] for line in ocr_result[0]])
-            expiry_dates = extract_expiry_dates(extracted_text)
-            expiry_info = calculate_days_to_expiry(expiry_dates)
-        else:
-            extracted_text = "No text detected"
-            expiry_dates = []
-            expiry_info = []
-
-        # Count objects
-        object_counts = {}
-        for box in results[0].boxes.data.cpu().numpy():
-            label = results[0].names[int(box[5])]
-            object_counts[label] = object_counts.get(label, 0) + 1
-
-        # Display results
-        st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected Image")
-        st.markdown(f"*Extracted Text:* {extracted_text}")
-        st.markdown(f"*Expiry Dates:*")
-        if expiry_info:
-            for info in expiry_info:
-                st.markdown(f"- {info}")
-        else:
-            st.markdown("None")
-        st.markdown("*Object Counts:*")
-        for label, count in object_counts.items():
-            st.markdown(f"- {label}: {count}")
-
-elif app_mode == "Fruit Freshness Detection":
-    st.header("Fruit Freshness Detection")
-    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+else:
+    st.header(app_mode)
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"], key=app_mode)
 
     if uploaded_file is not None:
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-        image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
+        st.session_state['uploaded_file'] = uploaded_file
 
-        # Preprocess and predict
-        img_array = preprocess_image(image)
-        predictions = fruit_model.predict(img_array)
-        predicted_class = np.argmax(predictions, axis=1)[0]
-        label = class_names[predicted_class]
-        confidence = predictions[0][predicted_class] * 100
-
-        # Display results
-        st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image")
-        st.markdown(f"*Label:* {label}")
-        st.markdown(f"*Confidence:* {confidence:.2f}%")
-
-elif app_mode == "Object Detection":
-    st.header("Object Detection")
-    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
-
-    if uploaded_file is not None:
-        file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
+    if st.session_state['uploaded_file'] is not None:
+        file_bytes = np.asarray(bytearray(st.session_state['uploaded_file'].read()), dtype=np.uint8)
         image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
 
-        # Object Detection
-        results = object_model.predict(source=image, stream=False)
-        detected_objects = []
-
-        for result in results:
-            boxes = result.boxes.data.cpu().numpy()
-            for box in boxes:
-                class_id = int(box[5])
-                confidence = box[4]  # Assuming the confidence score is at index 4
-                detected_objects.append((result.names[class_id], confidence))
-
-                # Draw bounding box and label on the image
-                x1, y1, x2, y2 = map(int, box[:4])
-                label = f"{result.names[class_id]} {confidence * 100:.2f}%"
-                cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
-                cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
-
-        # Convert the image back to RGB for display in Streamlit
-        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-        st.image(image_rgb, caption='Detected Objects', use_container_width=True)
-
-        # Count occurrences and average confidence of each object
-        object_data = {}
-        for obj, confidence in detected_objects:
-            if obj in object_data:
-                object_data[obj]['count'] += 1
-                object_data[obj]['total_confidence'] += confidence
+        if app_mode == "Brand & Text Detection":
+            # Brand detection
+            results = brand_model.predict(source=image, stream=False)
+            detected_image = results[0].plot()
+
+            # OCR for text extraction
+            _, img_buffer = cv2.imencode('.jpg', image)
+            ocr_result = ocr.ocr(img_buffer.tobytes())
+            if ocr_result and isinstance(ocr_result[0], list) and len(ocr_result[0]) > 0:
+                extracted_text = ' '.join([line[1][0] for line in ocr_result[0]])
+                expiry_dates = extract_expiry_dates(extracted_text)
+                expiry_info = calculate_days_to_expiry(expiry_dates)
+            else:
+                extracted_text = "No text detected"
+                expiry_dates = []
+                expiry_info = []
+
+            # Count objects
+            object_counts = {}
+            for box in results[0].boxes.data.cpu().numpy():
+                label = results[0].names[int(box[5])]
+                object_counts[label] = object_counts.get(label, 0) + 1
+
+            # Display results
+            st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected Image")
+            st.markdown(f"*Extracted Text:* {extracted_text}")
+            st.markdown(f"*Expiry Dates:*")
+            if expiry_info:
+                for info in expiry_info:
+                    st.markdown(f"- {info}")
             else:
-                object_data[obj] = {'count': 1, 'total_confidence': confidence}
+                st.markdown("None")
+            st.markdown("*Object Counts:*")
+            for label, count in object_counts.items():
+                st.markdown(f"- {label}: {count}")
+
+        elif app_mode == "Fruit Freshness Detection":
+            # Preprocess and predict
+            img_array = preprocess_image(image)
+            predictions = fruit_model.predict(img_array)
+            predicted_class = np.argmax(predictions, axis=1)[0]
+            label = class_names[predicted_class]
+            confidence = predictions[0][predicted_class] * 100
+
+            # Display results
+            st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image")
+            st.markdown(f"*Label:* {label}")
+            st.markdown(f"*Confidence:* {confidence:.2f}%")
+
+        elif app_mode == "Object Detection":
+            # Object Detection
+            results = object_model.predict(source=image, stream=False)
+            detected_objects = []
+
+            for result in results:
+                boxes = result.boxes.data.cpu().numpy()
+                for box in boxes:
+                    class_id = int(box[5])
+                    confidence = box[4]  # Assuming the confidence score is at index 4
+                    detected_objects.append((result.names[class_id], confidence))
+
+                    # Draw bounding box and label on the image
+                    x1, y1, x2, y2 = map(int, box[:4])
+                    label = f"{result.names[class_id]} {confidence * 100:.2f}%"
+                    cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+                    cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
+
+            # Convert the image back to RGB for display in Streamlit
+            image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            st.image(image_rgb, caption='Detected Objects', use_container_width=True)
+
+            # Count occurrences and average confidence of each object
+            object_data = {}
+            for obj, confidence in detected_objects:
+                if obj in object_data:
+                    object_data[obj]['count'] += 1
+                    object_data[obj]['total_confidence'] += confidence
+                else:
+                    object_data[obj] = {'count': 1, 'total_confidence': confidence}
 
-        # Prepare data for display
-        object_display_data = [
-            {'Object': obj, 'Count': data['count'], 'Average Confidence': data['total_confidence'] / data['count']}
-            for obj, data in object_data.items()
-        ]
+            # Prepare data for display
+            object_display_data = [
+                {'Object': obj, 'Count': data['count'], 'Average Confidence': data['total_confidence'] / data['count']}
+                for obj, data in object_data.items()
+            ]
 
-        # Display detected objects in a table with column names
-        st.write("Detected Objects and Counts:")
-        st.table(pd.DataFrame(object_display_data))
+            # Display detected objects in a table with column names
+            st.write("Detected Objects and Counts:")
+            st.table(pd.DataFrame(object_display_data))