Update app.py (#1)

- Update app.py (d21553c0cf1d1eac4126f910b1782ac7121e5b51)


Co-authored-by: Ratan Prakash Mishra <[email protected]>

app.py

@@ -1,597 +1,597 @@
-import streamlit as st
-from ultralytics import YOLO
-import tensorflow as tf  # Change this to import TensorFlow
-import numpy as np
-from PIL import Image, ImageOps, ImageDraw, ImageFont
-import pandas as pd
-import time
-from paddleocr import PaddleOCR, draw_ocr
-import re
-import dateparser
-import os
-import matplotlib.pyplot as plt
-
-# Initialize PaddleOCR model
-ocr = PaddleOCR(use_angle_cls=True, lang='en')
-# Define the class names based on your dataset
-class_names = [
-    'fresh_apple', 'fresh_banana', 'fresh_bitter_gourd', 'fresh_capsicum',
-    'fresh_orange', 'fresh_tomato', 'stale_apple', 'stale_banana',
-    'stale_bitter_gourd', 'stale_capsicum', 'stale_orange', 'stale_tomato'
-]
-
-# Team details
-team_members = [
-    {"name": "Aman Deep", "image": "aman.jpg"},  # Replace with actual paths to images
-    {"name": "Abhishek Kumar Choudhary", "image": "myimage.jpg"},
-    {"name": "Gaurav Lodhi", "image": "gaurav.jpg"},
-    {"name": "Anand Jha", "image": "anandimg.jpg"}
-]
-
-# Function to preprocess the images for the model
-from PIL import Image
-import numpy as np
-
-def preprocess_image(image):
-    """
-    Preprocess the input image for model prediction.
-
-    Args:
-        image (PIL.Image): Input image in PIL format.
-
-    Returns:
-        np.ndarray: Preprocessed image array ready for prediction.
-    """
-    try:
-        # Resize image to match model input size
-        img = image.resize((128, 128), Image.LANCZOS)  # Using LANCZOS filter for high-quality resizing
-
-        # Convert image to NumPy array
-        img_array = np.array(img)
-
-        # Check if the image is grayscale and convert to RGB if needed
-        if img_array.ndim == 2:  # Grayscale image
-            img_array = np.stack([img_array] * 3, axis=-1)  # Convert to 3-channel RGB
-        elif img_array.shape[2] == 1:  # Single-channel image
-            img_array = np.concatenate([img_array, img_array, img_array], axis=-1)  # Convert to RGB
-
-        # Normalize pixel values to [0, 1] range
-        img_array = img_array / 255.0
-
-        # Add batch dimension
-        img_array = np.expand_dims(img_array, axis=0)  # Shape: (1, 128, 128, 3)
-
-        return img_array
-
-    except Exception as e:
-        print(f"Error processing image: {e}")
-        return None  # Return None if there's an error
-
-
-# Function to create a high-quality circular mask for an image
-def make_image_circular1(img, size=(256, 256)):
-    img = img.resize(size, Image.LANCZOS)
-    mask = Image.new("L", size, 0)
-    draw = ImageDraw.Draw(mask)
-    draw.ellipse((0, 0) + size, fill=255)
-    output = ImageOps.fit(img, mask.size, centering=(0.5, 0.5))
-    output.putalpha(mask)  # Apply the mask as transparency
-    return output
-# Function to check if a file exists
-def file_exists(file_path):
-    return os.path.isfile(file_path)
-
-def make_image_circular(image):
-    # Create a circular mask
-    mask = Image.new("L", image.size, 0)
-    draw = ImageDraw.Draw(mask)
-    draw.ellipse((0, 0, image.size[0], image.size[1]), fill=255)
-
-    # Apply the mask to the image
-    circular_image = Image.new("RGB", image.size)
-    circular_image.paste(image.convert("RGBA"), (0, 0), mask)
-
-    return circular_image
-
-# Function to extract dates from recognized text using regex
-def extract_dates_with_dateparser(texts, result):
-    date_texts = []
-    date_boxes = []
-    date_scores = []
-    
-    def is_potential_date(text):
-        valid_date_pattern = r'^(0[1-9]|[12][0-9]|3[01])[-/.]?(0[1-9]|1[0-2])[-/.]?(\d{2}|\d{4})$|' \
-                             r'^(0[1-9]|[12][0-9]|3[01])[-/.]?[A-Za-z]{3}[-/.]?(\d{2}|\d{4})$|' \
-                             r'^(0[1-9]|1[0-2])[-/.]?(\d{2}|\d{4})$|' \
-                             r'^[A-Za-z]{3}[-/.]?(\d{2}|\d{4})$'
-        return bool(re.match(valid_date_pattern, text))
-
-    dates_found = []
-    for i, text in enumerate(texts):
-        if is_potential_date(text):  # Only process texts that are potential dates
-            parsed_date = dateparser.parse(text, settings={'DATE_ORDER': 'DMY'})
-            if parsed_date:
-                dates_found.append(parsed_date.strftime('%Y-%m-%d'))  # Store as 'YYYY-MM-DD'
-                date_texts.append(text)  # Store the original text
-                date_boxes.append(result[0][i][0])  # Store the bounding box
-                date_scores.append(result[0][i][1][1])  # Store confidence score
-    return dates_found, date_texts, date_boxes, date_scores
-
-# Function to display circular images in a matrix format
-def display_images_in_grid(images, max_images_per_row=4):
-    num_images = len(images)
-    num_rows = (num_images + max_images_per_row - 1) // max_images_per_row  # Calculate number of rows
-
-    for i in range(num_rows):
-        cols = st.columns(min(max_images_per_row, num_images - i * max_images_per_row))
-        for j, img in enumerate(images[i * max_images_per_row:(i + 1) * max_images_per_row]):
-            with cols[j]:
-                st.image(img, use_column_width=True)
-
-# Function to display team members in circular format
-def display_team_members(members, max_members_per_row=4):
-    num_members = len(members)
-    num_rows = (num_members + max_members_per_row - 1) // max_members_per_row  # Calculate number of rows
-
-    for i in range(num_rows):
-        cols = st.columns(min(max_members_per_row, num_members - i * max_members_per_row))
-        for j, member in enumerate(members[i * max_members_per_row:(i + 1) * max_members_per_row]):
-            with cols[j]:
-                img = Image.open(member["image"])  # Load the image
-                circular_img = make_image_circular(img)  # Convert to circular format
-                st.image(circular_img, use_column_width=True)  # Display the circular image
-                st.write(member["name"])  # Display the name below the image
-
-# Title and description
-st.title("Amazon Sambhav")
-# Team Details with links
-st.sidebar.title("Amazon Sambhav")
-st.sidebar.write("DELHI TECHNOLOGICAL UNIVERSITY")
-
-# Navbar with task tabs
-st.sidebar.title("Navigation")
-st.sidebar.write("Team Name: aman.dp121")
-app_mode = st.sidebar.selectbox("Choose the task", ["Welcome","Project Details", "Task 1","Team Details"])
-if app_mode == "Welcome":
-    # Navigation Menu
-    st.write("# Welcome to Amazon Sambhav! 🎉")
-
-    # Example for adding a local video
-    video_file = open('Finalist.mp4', 'rb')  # Replace with the path to your video file
-    video_bytes = video_file.read()
-    # Embed the video using st.video()
-    st.video(video_bytes) 
-
-    # Add a welcome image
-    welcome_image = Image.open("grid_banner.jpg")  # Replace with the path to your welcome image
-    st.image(welcome_image, use_column_width=True)  # Display the welcome image
-    
-elif app_mode=="Project Details":
-    st.markdown("""
-    ## Navigation
-    - [Project Overview](#project-overview)
-    - [Proposal Round](#proposal-round)
-    - [Problem Statement](#problem-statement)
-    - [Proposed Solution](#proposed-solution)
-    """)
-    # Project Overview
-    st.write("## Project Overview:")
-    st.write(""" 
-    1. **OCR to Extract Details** (20%):
-        - Use OCR to read brand details, pack size, brand name, etc.
-        - Train the model to read details from various products, including FMCG, OTC items, health supplements, personal care, and household items.
-    
-    2. **Using OCR for Expiry Date Details** (10%):
-        - Validate expiry dates using OCR to read expiry and MRP details printed on items.
-    
-    3. **Image Recognition for Brand Recognition and Counting** (30%):
-        - Use machine learning to recognize brands and count product quantities from images.
-    
-    4. **Detecting Freshness of Fresh Produce** (40%):
-        - Assess the freshness of fruits and vegetables by analyzing various visual cues and patterns.
-    """)
-    
-    st.write(""" 
-    Our project aims to leverage OCR and image recognition to enhance product packaging analysis and freshness detection. 
-    """)
-
-    # Proposal Round
-    st.write("## Proposal Round:")
-    st.write(""" 
-    **Format:** Use Case Submission & Code Review
-    - Selected teams will submit detailed use case scenarios they plan to solve.
-    - The submission should include a proposal outlining their approach and the code developed so far.
-    - The GRID team will provide a set of images for testing the model.
-    - Since this is an elimination stage, participants are encouraged to submit a video simulation of their solution on the image set provided to them, ensuring they can clearly articulate what they have solved. 
-    - Teams working on detecting the freshness of produce may choose any fresh fruit/vegetable/bread, etc., and submit the freshness index based on the model.
-    - The video will help demonstrate the effectiveness of their approach and provide a visual representation of their solution.
-    
-    Teams with the most comprehensive and innovative proposals will proceed to the final stage.
-    """)
-
-    # Problem Statement
-    st.write("## Problem Statement:")
-    st.write(""" 
-    In today’s fast-paced retail environment, ensuring product quality and freshness is crucial for customer satisfaction. The Amazon Sambhav Challenge aims to address this issue by leveraging technology to enhance product packaging analysis and freshness detection. 
-
-    Traditional methods of checking freshness often involve manual inspection, which can be time-consuming and prone to human error. Furthermore, with the increasing variety of products available, a more automated and reliable solution is needed to streamline this process.
-
-    Our project focuses on developing an advanced system that utilizes Optical Character Recognition (OCR) and image recognition techniques to automate the extraction of product details from packaging. This will not only improve accuracy but also increase efficiency in assessing product freshness.
-    """)
-
-    # Proposed Solution
-    st.write("## Proposed Solution:")
-    st.write(""" 
-    Our solution is designed to tackle the problem by implementing the following key components:
-
-    ### 1. OCR for Product Detail Extraction
-    We will use OCR technology to accurately extract critical information from product packaging, including:
-    - Brand name
-    - Pack size
-    - Expiry date
-    - MRP details
-
-    This will allow for real-time analysis of product information, ensuring that customers receive accurate data about their purchases.
-
-    ### 2. Freshness Detection using Image Recognition
-    In conjunction with OCR, our model will utilize image recognition to assess the freshness of fruits, vegetables, and other perishable items. The model will be trained to classify products based on their appearance, detecting signs of spoilage and degradation. 
-
-    ### 3. Data Validation and Reporting
-    Our system will not only extract data but also validate expiry dates against the current date to ensure product safety. The results will be compiled into a user-friendly report that can be easily interpreted by retail staff.
-
-    ### 4. Video Simulation
-    To effectively demonstrate our solution, we will create a video simulation showcasing the functionality of our system. This will include real-time examples of how our model processes images and extracts relevant information.
-
-    ### 5. Proposal Submission
-    As part of the proposal round, we will provide a comprehensive submission outlining our approach, methodology, and the code developed thus far. This submission will highlight the effectiveness of our solution and our readiness to proceed to the final stage of the challenge.
-    
-    Our team is committed to delivering a robust solution that not only meets but exceeds the expectations of the Amazon Sambhav Challenge.
-    """)
-
-elif app_mode == "Team Details":
-    st.write("## Meet Our Team:")
-    display_team_members(team_members)
-    st.write("Delhi Technological University")
-
-elif app_mode == "Task 1":
-    st.write("## Task 1: 🖼️ OCR to Extract Details 📄")
-    st.write("Using OCR to extract details from product packaging material, including brand name and pack size.")
-    
-    # File uploader for images (supports multiple files)
-    uploaded_files = st.file_uploader("Upload images of products", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
-
-    if uploaded_files:
-        st.write("### Uploaded Images in Circular Format:")
-        circular_images = []
-
-        for uploaded_file in uploaded_files:
-            img = Image.open(uploaded_file)
-            circular_img = make_image_circular(img)  # Create circular images
-            circular_images.append(circular_img)
-
-        # Display the circular images in a matrix/grid format
-        display_images_in_grid(circular_images, max_images_per_row=4)
-
-        # Function to simulate loading process with a progress bar
-        def simulate_progress():
-            progress_bar = st.progress(0)
-            for percent_complete in range(100):
-                time.sleep(0.02)
-                progress_bar.progress(percent_complete + 1)
-        # Function to remove gibberish using regex (removes non-alphanumeric chars, filters out very short text)
-        def clean_text(text):
-            # Keep text with letters, digits, and spaces, and remove short/irrelevant text
-            return re.sub(r'[^a-zA-Z0-9\s]', '', text).strip()
-
-        # Function to extract the most prominent text (product name) and other details
-        def extract_product_info(results):
-            product_name = ""
-            product_details = ""
-            largest_text_size = 0
-
-            for line in results:
-                for box in line:
-                    text, confidence = box[1][0], box[1][1]
-                    text_size = box[0][2][1] - box[0][0][1]  # Calculate height of the text box
-
-                    # Clean the text to avoid gibberish
-                    clean_text_line = clean_text(text)
-
-                    if confidence > 0.7 and len(clean_text_line) > 2:  # Only consider confident, meaningful text
-                        if text_size > largest_text_size:  # Assume the largest text is the product name
-                            largest_text_size = text_size
-                            product_name = clean_text_line
-                        else:
-                            product_details += clean_text_line + " "
-
-            return product_name, product_details.strip()
-        if st.button("Start Analysis"):
-            simulate_progress()
-            # Loop through each uploaded image and process them
-            for uploaded_image in uploaded_files:
-                # Load the uploaded image
-                image = Image.open(uploaded_image)
-                # st.image(image, caption=f'Uploaded Image: {uploaded_image.name}', use_column_width=True)
-
-                # Convert image to numpy array for OCR processing
-                img_array = np.array(image)
-
-                # Perform OCR on the image
-                st.write(f"Extracting details from {uploaded_image.name}...")
-                result = ocr.ocr(img_array, cls=True)
-
-                # Process the OCR result to extract product name and properties
-                product_name, product_details = extract_product_info(result)
-
-                # UI display for single image product details
-                st.markdown("---")
-                st.markdown(f"### **Product Name:** `{product_name}`")
-                st.write(f"**Product Properties:** {product_details}")
-                st.markdown("---")
-        
-    else:
-        st.write("Please upload images to extract product details.")
-
-elif app_mode == "Task 2":
-    st.write("## Task 2:📅 Expiry Date Validation ✅")
-    st.write("Use OCR to get expiry and MRP details printed on items.")
-    # File uploader for images (supports multiple files)
-    uploaded_files = st.file_uploader("Upload images of products containing expiry date", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
-
-    if uploaded_files:
-        st.write("### Uploaded Images in Circular Format:")
-        circular_images = []
-
-        for uploaded_file in uploaded_files:
-            img = Image.open(uploaded_file)
-            circular_img = make_image_circular(img)  # Create circular images
-            circular_images.append(circular_img)
-
-        # Display the circular images in a matrix/grid format
-        display_images_in_grid(circular_images, max_images_per_row=4)
-
-        # Function to simulate loading process with a progress bar
-        def simulate_progress():
-            progress_bar = st.progress(0)
-            for percent_complete in range(100):
-                time.sleep(0.02)
-                progress_bar.progress(percent_complete + 1)
-
-        for idx, uploaded_file in enumerate(uploaded_files):
-            image = Image.open(uploaded_file)
-            img_array = np.array(image)
-            result = ocr.ocr(img_array, cls=True)
-
-            if result and result[0]:
-                # Extract recognized texts
-                recognized_texts = [line[1][0] for line in result[0]]
-
-                # Clean up recognized texts by removing extra spaces and standardizing formats
-                cleaned_texts = []
-                for text in recognized_texts:
-                    cleaned_text = re.sub(r'\s+', ' ', text.strip())  # Replace multiple spaces with a single space
-                    cleaned_text = cleaned_text.replace('.', '').replace(',', '')  # Remove dots and commas for date detection
-                    cleaned_texts.append(cleaned_text)
-
-                # Extract dates from recognized texts
-                extracted_dates, date_texts, date_boxes, date_scores = extract_dates_with_dateparser(cleaned_texts, result)
-
-                if extracted_dates:
-                    # Display extracted dates
-                    st.write("**Extracted Dates**:")
-                    for date, text in zip(extracted_dates, date_texts):
-                        st.write(f"Detected Date: **{date}**, Original Text: *{text}*")
-                else:
-                    st.write("No valid dates found in the image.")
-
-                # Option to visualize the bounding boxes on the image
-                if st.checkbox(f"Show image with highlighted dates for {uploaded_file.name}", key=f"highlight_{idx}"):
-                    # Draw the OCR results on the image
-                    image_with_boxes = draw_ocr(image, date_boxes, date_texts, date_scores,font_path='CedarvilleCursive-Regular.ttf')  # Removed font path
-
-                    # Display the image with highlighted boxes
-                    plt.figure(figsize=(10, 10))
-                    plt.imshow(image_with_boxes)
-                    plt.axis('off')  # Hide axes
-                    st.pyplot(plt)
-            else:
-                st.write("No text detected in the image.")
-
-
-def make_image_circular1(image):
-    # Create a circular mask
-    mask = Image.new("L", image.size, 0)
-    draw = ImageDraw.Draw(mask)
-    draw.ellipse((0, 0, image.size[0], image.size[1]), fill=255)
-
-    # Apply the mask to the image
-    circular_image = Image.new("RGB", image.size)
-    circular_image.paste(image.convert("RGBA"), (0, 0), mask)
-
-    return circular_image
-
-def display_images_in_grid1(images, max_images_per_row=4):
-    rows = (len(images) + max_images_per_row - 1) // max_images_per_row  # Calculate number of rows needed
-
-    for i in range(0, len(images), max_images_per_row):
-        cols_to_show = images[i:i + max_images_per_row]
-        
-        # Prepare to display in a grid format
-        cols = st.columns(max_images_per_row)  # Create columns dynamically
-        
-        for idx, img in enumerate(cols_to_show):
-            img = img.convert("RGB")  # Ensure the image is in RGB mode
-            
-            if idx < len(cols):
-                cols[idx].image(img, use_column_width=True)
-
-# Initialize your Streamlit app
-if app_mode == "Task 3":
-    st.write("## Task 3: Image Recognition 📸 and IR-Based Counting 📊")
-    
-    # File uploader for images (supports multiple files)
-    uploaded_files = st.file_uploader("Upload images of fruits, vegetables, or products for brand recognition and freshness detection", 
-                                      type=["jpeg", "png", "jpg"], accept_multiple_files=True)
-    if uploaded_files:
-        st.write("### Uploaded Images:")
-        # Load the pre-trained YOLOv8 model
-        model = YOLO('yolov9c.pt')  # Adjust path to your YOLO model if needed
-
-        # Initialize a dictionary to store counts of detected products
-        product_count_dict = {}
-        circular_images = []
-        images=[]
-
-        for uploaded_file in uploaded_files:
-            img = Image.open(uploaded_file)
-            circular_img = make_image_circular(img)  # Create circular images
-            circular_images.append(circular_img)
-            images.append(img)
-
-        # Display the circular images in a matrix/grid format
-        display_images_in_grid(circular_images, max_images_per_row=4)
-
-        detected_images = []
-        
-        for idx, image in enumerate(images):
-            # Run object detection
-            results = model(image)
-
-            # Initialize counts for this image
-            image_counts = {}
-
-            # Display results with bounding boxes
-            for result in results:
-                img_with_boxes = result.plot()  # Get image with bounding boxes
-                detected_images.append(make_image_circular(image.resize((150, 150))))  # Resize and make circular
-                
-                # Display detected object counts per class
-                counts = result.boxes.cls.tolist()  # Extract class IDs
-                class_counts = {int(cls): counts.count(cls) for cls in set(counts)}
-                
-                # Update the image counts for this image
-                for cls_id, count in class_counts.items():
-                    product_name = result.names[cls_id]  # Get the product name from class ID
-                    image_counts[product_name] = count
-                
-                # Aggregate counts into the main product count dictionary
-                for product, count in image_counts.items():
-                    if product in product_count_dict:
-                        product_count_dict[product] += count
-                    else:
-                        product_count_dict[product] = count
-
-            # Option to visualize the bounding boxes on the image
-            if st.checkbox(f"Show image with highlighted boxes for image {idx + 1}", key=f"checkbox_{idx}"):
-                st.image(img_with_boxes, caption="Image with Highlighted Boxes", use_column_width=True)
-
-        # Display the total counts as a bar chart
-        st.write("### Total Product Counts Across All Images:")
-        if product_count_dict:
-            product_count_df = pd.DataFrame(product_count_dict.items(), columns=["Product", "Count"])
-            st.bar_chart(product_count_df.set_index("Product"))
-        else:
-            st.write("No products detected.")
-
-elif app_mode == "Task 4":
-    st.write("## Task 4: 🍏 Fruit and Vegetable Freshness Detector 🍅")
-    # Load the trained model
-    try:
-        model = tf.keras.models.load_model('fruit_freshness_model.h5')  # Using TensorFlow to load the model
-        st.success("Model loaded successfully!")
-    except Exception as e:
-        st.error(f"Error loading model: {e}")
-    
-    # File uploader for images (supports multiple files)
-    uploaded_files = st.file_uploader("Upload images of fruits/vegetables", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
-
-    if uploaded_files:
-        st.write("### Uploaded Images in Circular Format:")
-        circular_images = []
-        images=[]
-
-        for uploaded_file in uploaded_files:
-            img = Image.open(uploaded_file)
-            circular_img = make_image_circular(img)  # Create circular images
-            circular_images.append(circular_img)
-            images.append(img)
-
-        # Display the circular images in a matrix/grid format
-        display_images_in_grid(circular_images, max_images_per_row=4)
-
-        # Function to simulate loading process with a progress bar
-        def simulate_progress():
-            progress_bar = st.progress(0)
-            for percent_complete in range(100):
-                time.sleep(0.02)
-                progress_bar.progress(percent_complete + 1)
-
-        # Create an empty DataFrame to hold the image name and prediction results
-        results_df = pd.DataFrame(columns=["Image", "Prediction"])
-
-        # Create a dictionary to count the occurrences of each class
-        class_counts = {class_name: 0 for class_name in class_names}
-
-        # Button to initiate predictions
-        if st.button("Run Prediction"):
-            # Display progress bar
-            simulate_progress()
-
-            for idx, img in enumerate(images):  # Use circular images for predictions
-                img_array = preprocess_image(img.convert('RGB'))  # Convert to RGB
-
-                try:
-                    # Perform the prediction
-                    prediction = model.predict(img_array)
-
-                    # Get the class with the highest probability
-                    result = class_names[np.argmax(prediction)]
-                    st.success(f'Prediction for Image {idx + 1}: **{result}**')
-
-                    # Increment the class count
-                    class_counts[result] += 1
-
-                    # Add the result to the DataFrame
-                    result_data = pd.DataFrame({"Image": [uploaded_files[idx].name], "Prediction": [result]})
-                    results_df = pd.concat([results_df, result_data], ignore_index=True)
-
-                except Exception as e:
-                    st.error(f"Error occurred during prediction: {e}")
-
-            # Display class distribution as a bar chart
-            st.write("### Class Distribution:")
-            class_counts_df = pd.DataFrame(list(class_counts.items()), columns=['Class', 'Count'])
-            st.bar_chart(class_counts_df.set_index('Class'))
-
-            # Option to download the prediction results as a CSV file
-            st.write("### Download Results:")
-            csv = results_df.to_csv(index=False).encode('utf-8')
-            st.download_button(
-                label="Download prediction results as CSV",
-                data=csv,
-                file_name='prediction_results.csv',
-                mime='text/csv',
-            )
-
-            # Display the dataframe after the graph
-            st.write("### Prediction Data:")
-            st.dataframe(results_df)
-
-# Footer with animation
-st.markdown(""" 
-    <style>
-        @keyframes fade-in {
-            from { opacity: 0; }
-            to { opacity: 1;}
-        }
-        .footer {
-            text-align: center;
-            font-size: 1.1em;
-            animation: fade-in 2s;
-            padding-top: 2rem;
-        }
-    </style>
-    <div class="footer">
-        <p>© 2024 Amazon Sambhav Challenge. All rights reserved.</p>
-    </div>
-""", unsafe_allow_html=True)
+import streamlit as st
+from ultralytics import YOLO
+import tensorflow as tf  # Change this to import TensorFlow
+import numpy as np
+from PIL import Image, ImageOps, ImageDraw, ImageFont
+import pandas as pd
+import time
+from paddleocr import PaddleOCR, draw_ocr
+import re
+import dateparser
+import os
+import matplotlib.pyplot as plt
+
+# Initialize PaddleOCR model
+ocr = PaddleOCR(use_angle_cls=True, lang='en')
+# Define the class names based on your dataset
+class_names = [
+    'fresh_apple', 'fresh_banana', 'fresh_bitter_gourd', 'fresh_capsicum',
+    'fresh_orange', 'fresh_tomato', 'stale_apple', 'stale_banana',
+    'stale_bitter_gourd', 'stale_capsicum', 'stale_orange', 'stale_tomato'
+]
+
+# Team details
+team_members = [
+    {"name": "Aman Deep", "image": "aman.jpg"},  # Replace with actual paths to images
+    {"name": "Nandini", "image": "myimage.jpg"},
+    {"name": "Abhay Sharma", "image": "gaurav.jpg"},
+    {"name": "Ratan Prakash Mishra", "image": "anandimg.jpg"}
+]
+
+# Function to preprocess the images for the model
+from PIL import Image
+import numpy as np
+
+def preprocess_image(image):
+    """
+    Preprocess the input image for model prediction.
+
+    Args:
+        image (PIL.Image): Input image in PIL format.
+
+    Returns:
+        np.ndarray: Preprocessed image array ready for prediction.
+    """
+    try:
+        # Resize image to match model input size
+        img = image.resize((128, 128), Image.LANCZOS)  # Using LANCZOS filter for high-quality resizing
+
+        # Convert image to NumPy array
+        img_array = np.array(img)
+
+        # Check if the image is grayscale and convert to RGB if needed
+        if img_array.ndim == 2:  # Grayscale image
+            img_array = np.stack([img_array] * 3, axis=-1)  # Convert to 3-channel RGB
+        elif img_array.shape[2] == 1:  # Single-channel image
+            img_array = np.concatenate([img_array, img_array, img_array], axis=-1)  # Convert to RGB
+
+        # Normalize pixel values to [0, 1] range
+        img_array = img_array / 255.0
+
+        # Add batch dimension
+        img_array = np.expand_dims(img_array, axis=0)  # Shape: (1, 128, 128, 3)
+
+        return img_array
+
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        return None  # Return None if there's an error
+
+
+# Function to create a high-quality circular mask for an image
+def make_image_circular1(img, size=(256, 256)):
+    img = img.resize(size, Image.LANCZOS)
+    mask = Image.new("L", size, 0)
+    draw = ImageDraw.Draw(mask)
+    draw.ellipse((0, 0) + size, fill=255)
+    output = ImageOps.fit(img, mask.size, centering=(0.5, 0.5))
+    output.putalpha(mask)  # Apply the mask as transparency
+    return output
+# Function to check if a file exists
+def file_exists(file_path):
+    return os.path.isfile(file_path)
+
+def make_image_circular(image):
+    # Create a circular mask
+    mask = Image.new("L", image.size, 0)
+    draw = ImageDraw.Draw(mask)
+    draw.ellipse((0, 0, image.size[0], image.size[1]), fill=255)
+
+    # Apply the mask to the image
+    circular_image = Image.new("RGB", image.size)
+    circular_image.paste(image.convert("RGBA"), (0, 0), mask)
+
+    return circular_image
+
+# Function to extract dates from recognized text using regex
+def extract_dates_with_dateparser(texts, result):
+    date_texts = []
+    date_boxes = []
+    date_scores = []
+    
+    def is_potential_date(text):
+        valid_date_pattern = r'^(0[1-9]|[12][0-9]|3[01])[-/.]?(0[1-9]|1[0-2])[-/.]?(\d{2}|\d{4})$|' \
+                             r'^(0[1-9]|[12][0-9]|3[01])[-/.]?[A-Za-z]{3}[-/.]?(\d{2}|\d{4})$|' \
+                             r'^(0[1-9]|1[0-2])[-/.]?(\d{2}|\d{4})$|' \
+                             r'^[A-Za-z]{3}[-/.]?(\d{2}|\d{4})$'
+        return bool(re.match(valid_date_pattern, text))
+
+    dates_found = []
+    for i, text in enumerate(texts):
+        if is_potential_date(text):  # Only process texts that are potential dates
+            parsed_date = dateparser.parse(text, settings={'DATE_ORDER': 'DMY'})
+            if parsed_date:
+                dates_found.append(parsed_date.strftime('%Y-%m-%d'))  # Store as 'YYYY-MM-DD'
+                date_texts.append(text)  # Store the original text
+                date_boxes.append(result[0][i][0])  # Store the bounding box
+                date_scores.append(result[0][i][1][1])  # Store confidence score
+    return dates_found, date_texts, date_boxes, date_scores
+
+# Function to display circular images in a matrix format
+def display_images_in_grid(images, max_images_per_row=4):
+    num_images = len(images)
+    num_rows = (num_images + max_images_per_row - 1) // max_images_per_row  # Calculate number of rows
+
+    for i in range(num_rows):
+        cols = st.columns(min(max_images_per_row, num_images - i * max_images_per_row))
+        for j, img in enumerate(images[i * max_images_per_row:(i + 1) * max_images_per_row]):
+            with cols[j]:
+                st.image(img, use_column_width=True)
+
+# Function to display team members in circular format
+def display_team_members(members, max_members_per_row=4):
+    num_members = len(members)
+    num_rows = (num_members + max_members_per_row - 1) // max_members_per_row  # Calculate number of rows
+
+    for i in range(num_rows):
+        cols = st.columns(min(max_members_per_row, num_members - i * max_members_per_row))
+        for j, member in enumerate(members[i * max_members_per_row:(i + 1) * max_members_per_row]):
+            with cols[j]:
+                img = Image.open(member["image"])  # Load the image
+                circular_img = make_image_circular(img)  # Convert to circular format
+                st.image(circular_img, use_column_width=True)  # Display the circular image
+                st.write(member["name"])  # Display the name below the image
+
+# Title and description
+st.title("Amazon Smbhav")
+# Team Details with links
+st.sidebar.title("Amazon Smbhav")
+st.sidebar.write("DELHI TECHNOLOGICAL UNIVERSITY")
+
+# Navbar with task tabs
+st.sidebar.title("Navigation")
+st.sidebar.write("Team Name: aman.dp121")
+app_mode = st.sidebar.selectbox("Choose the task", ["Welcome","Project Details", "Task 1","Team Details"])
+if app_mode == "Welcome":
+    # Navigation Menu
+    st.write("# Welcome to Amazon Smbhav! 🎉")
+
+    # Example for adding a local video
+    video_file = open('Finalist.mp4', 'rb')  # Replace with the path to your video file
+    video_bytes = video_file.read()
+    # Embed the video using st.video()
+    st.video(video_bytes) 
+
+    # Add a welcome image
+    welcome_image = Image.open("grid_banner.jpg")  # Replace with the path to your welcome image
+    st.image(welcome_image, use_column_width=True)  # Display the welcome image
+    
+elif app_mode=="Project Details":
+    st.markdown("""
+    ## Navigation
+    - [Project Overview](#project-overview)
+    - [Proposal Round](#proposal-round)
+    - [Problem Statement](#problem-statement)
+    - [Proposed Solution](#proposed-solution)
+    """)
+    # Project Overview
+    st.write("## Project Overview:")
+    st.write(""" 
+    1. **OCR to Extract Details** (20%):
+        - Use OCR to read brand details, pack size, brand name, etc.
+        - Train the model to read details from various products, including FMCG, OTC items, health supplements, personal care, and household items.
+    
+    2. **Using OCR for Expiry Date Details** (10%):
+        - Validate expiry dates using OCR to read expiry and MRP details printed on items.
+    
+    3. **Image Recognition for Brand Recognition and Counting** (30%):
+        - Use machine learning to recognize brands and count product quantities from images.
+    
+    4. **Detecting Freshness of Fresh Produce** (40%):
+        - Assess the freshness of fruits and vegetables by analyzing various visual cues and patterns.
+    """)
+    
+    st.write(""" 
+    Our project aims to leverage OCR and image recognition to enhance product packaging analysis and freshness detection. 
+    """)
+
+    # Proposal Round
+    st.write("## Proposal Round:")
+    st.write(""" 
+    **Format:** Use Case Submission & Code Review
+    - Selected teams will submit detailed use case scenarios they plan to solve.
+    - The submission should include a proposal outlining their approach and the code developed so far.
+    - The GRID team will provide a set of images for testing the model.
+    - Since this is an elimination stage, participants are encouraged to submit a video simulation of their solution on the image set provided to them, ensuring they can clearly articulate what they have solved. 
+    - Teams working on detecting the freshness of produce may choose any fresh fruit/vegetable/bread, etc., and submit the freshness index based on the model.
+    - The video will help demonstrate the effectiveness of their approach and provide a visual representation of their solution.
+    
+    Teams with the most comprehensive and innovative proposals will proceed to the final stage.
+    """)
+
+    # Problem Statement
+    st.write("## Problem Statement:")
+    st.write(""" 
+    In today’s fast-paced retail environment, ensuring product quality and freshness is crucial for customer satisfaction. The Amazon Sambhav Challenge aims to address this issue by leveraging technology to enhance product packaging analysis and freshness detection. 
+
+    Traditional methods of checking freshness often involve manual inspection, which can be time-consuming and prone to human error. Furthermore, with the increasing variety of products available, a more automated and reliable solution is needed to streamline this process.
+
+    Our project focuses on developing an advanced system that utilizes Optical Character Recognition (OCR) and image recognition techniques to automate the extraction of product details from packaging. This will not only improve accuracy but also increase efficiency in assessing product freshness.
+    """)
+
+    # Proposed Solution
+    st.write("## Proposed Solution:")
+    st.write(""" 
+    Our solution is designed to tackle the problem by implementing the following key components:
+
+    ### 1. OCR for Product Detail Extraction
+    We will use OCR technology to accurately extract critical information from product packaging, including:
+    - Brand name
+    - Pack size
+    - Expiry date
+    - MRP details
+
+    This will allow for real-time analysis of product information, ensuring that customers receive accurate data about their purchases.
+
+    ### 2. Freshness Detection using Image Recognition
+    In conjunction with OCR, our model will utilize image recognition to assess the freshness of fruits, vegetables, and other perishable items. The model will be trained to classify products based on their appearance, detecting signs of spoilage and degradation. 
+
+    ### 3. Data Validation and Reporting
+    Our system will not only extract data but also validate expiry dates against the current date to ensure product safety. The results will be compiled into a user-friendly report that can be easily interpreted by retail staff.
+
+    ### 4. Video Simulation
+    To effectively demonstrate our solution, we will create a video simulation showcasing the functionality of our system. This will include real-time examples of how our model processes images and extracts relevant information.
+
+    ### 5. Proposal Submission
+    As part of the proposal round, we will provide a comprehensive submission outlining our approach, methodology, and the code developed thus far. This submission will highlight the effectiveness of our solution and our readiness to proceed to the final stage of the challenge.
+    
+    Our team is committed to delivering a robust solution that not only meets but exceeds the expectations of the Amazon Sambhav Challenge.
+    """)
+
+elif app_mode == "Team Details":
+    st.write("## Meet Our Team:")
+    display_team_members(team_members)
+    st.write("Delhi Technological University")
+
+elif app_mode == "Task 1":
+    st.write("## Task 1: 🖼️ OCR to Extract Details 📄")
+    st.write("Using OCR to extract details from product packaging material, including brand name and pack size.")
+    
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of products", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+
+    if uploaded_files:
+        st.write("### Uploaded Images in Circular Format:")
+        circular_images = []
+
+        for uploaded_file in uploaded_files:
+            img = Image.open(uploaded_file)
+            circular_img = make_image_circular(img)  # Create circular images
+            circular_images.append(circular_img)
+
+        # Display the circular images in a matrix/grid format
+        display_images_in_grid(circular_images, max_images_per_row=4)
+
+        # Function to simulate loading process with a progress bar
+        def simulate_progress():
+            progress_bar = st.progress(0)
+            for percent_complete in range(100):
+                time.sleep(0.02)
+                progress_bar.progress(percent_complete + 1)
+        # Function to remove gibberish using regex (removes non-alphanumeric chars, filters out very short text)
+        def clean_text(text):
+            # Keep text with letters, digits, and spaces, and remove short/irrelevant text
+            return re.sub(r'[^a-zA-Z0-9\s]', '', text).strip()
+
+        # Function to extract the most prominent text (product name) and other details
+        def extract_product_info(results):
+            product_name = ""
+            product_details = ""
+            largest_text_size = 0
+
+            for line in results:
+                for box in line:
+                    text, confidence = box[1][0], box[1][1]
+                    text_size = box[0][2][1] - box[0][0][1]  # Calculate height of the text box
+
+                    # Clean the text to avoid gibberish
+                    clean_text_line = clean_text(text)
+
+                    if confidence > 0.7 and len(clean_text_line) > 2:  # Only consider confident, meaningful text
+                        if text_size > largest_text_size:  # Assume the largest text is the product name
+                            largest_text_size = text_size
+                            product_name = clean_text_line
+                        else:
+                            product_details += clean_text_line + " "
+
+            return product_name, product_details.strip()
+        if st.button("Start Analysis"):
+            simulate_progress()
+            # Loop through each uploaded image and process them
+            for uploaded_image in uploaded_files:
+                # Load the uploaded image
+                image = Image.open(uploaded_image)
+                # st.image(image, caption=f'Uploaded Image: {uploaded_image.name}', use_column_width=True)
+
+                # Convert image to numpy array for OCR processing
+                img_array = np.array(image)
+
+                # Perform OCR on the image
+                st.write(f"Extracting details from {uploaded_image.name}...")
+                result = ocr.ocr(img_array, cls=True)
+
+                # Process the OCR result to extract product name and properties
+                product_name, product_details = extract_product_info(result)
+
+                # UI display for single image product details
+                st.markdown("---")
+                st.markdown(f"### **Product Name:** `{product_name}`")
+                st.write(f"**Product Properties:** {product_details}")
+                st.markdown("---")
+        
+    else:
+        st.write("Please upload images to extract product details.")
+
+elif app_mode == "Task 2":
+    st.write("## Task 2:📅 Expiry Date Validation ✅")
+    st.write("Use OCR to get expiry and MRP details printed on items.")
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of products containing expiry date", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+
+    if uploaded_files:
+        st.write("### Uploaded Images in Circular Format:")
+        circular_images = []
+
+        for uploaded_file in uploaded_files:
+            img = Image.open(uploaded_file)
+            circular_img = make_image_circular(img)  # Create circular images
+            circular_images.append(circular_img)
+
+        # Display the circular images in a matrix/grid format
+        display_images_in_grid(circular_images, max_images_per_row=4)
+
+        # Function to simulate loading process with a progress bar
+        def simulate_progress():
+            progress_bar = st.progress(0)
+            for percent_complete in range(100):
+                time.sleep(0.02)
+                progress_bar.progress(percent_complete + 1)
+
+        for idx, uploaded_file in enumerate(uploaded_files):
+            image = Image.open(uploaded_file)
+            img_array = np.array(image)
+            result = ocr.ocr(img_array, cls=True)
+
+            if result and result[0]:
+                # Extract recognized texts
+                recognized_texts = [line[1][0] for line in result[0]]
+
+                # Clean up recognized texts by removing extra spaces and standardizing formats
+                cleaned_texts = []
+                for text in recognized_texts:
+                    cleaned_text = re.sub(r'\s+', ' ', text.strip())  # Replace multiple spaces with a single space
+                    cleaned_text = cleaned_text.replace('.', '').replace(',', '')  # Remove dots and commas for date detection
+                    cleaned_texts.append(cleaned_text)
+
+                # Extract dates from recognized texts
+                extracted_dates, date_texts, date_boxes, date_scores = extract_dates_with_dateparser(cleaned_texts, result)
+
+                if extracted_dates:
+                    # Display extracted dates
+                    st.write("**Extracted Dates**:")
+                    for date, text in zip(extracted_dates, date_texts):
+                        st.write(f"Detected Date: **{date}**, Original Text: *{text}*")
+                else:
+                    st.write("No valid dates found in the image.")
+
+                # Option to visualize the bounding boxes on the image
+                if st.checkbox(f"Show image with highlighted dates for {uploaded_file.name}", key=f"highlight_{idx}"):
+                    # Draw the OCR results on the image
+                    image_with_boxes = draw_ocr(image, date_boxes, date_texts, date_scores,font_path='CedarvilleCursive-Regular.ttf')  # Removed font path
+
+                    # Display the image with highlighted boxes
+                    plt.figure(figsize=(10, 10))
+                    plt.imshow(image_with_boxes)
+                    plt.axis('off')  # Hide axes
+                    st.pyplot(plt)
+            else:
+                st.write("No text detected in the image.")
+
+
+def make_image_circular1(image):
+    # Create a circular mask
+    mask = Image.new("L", image.size, 0)
+    draw = ImageDraw.Draw(mask)
+    draw.ellipse((0, 0, image.size[0], image.size[1]), fill=255)
+
+    # Apply the mask to the image
+    circular_image = Image.new("RGB", image.size)
+    circular_image.paste(image.convert("RGBA"), (0, 0), mask)
+
+    return circular_image
+
+def display_images_in_grid1(images, max_images_per_row=4):
+    rows = (len(images) + max_images_per_row - 1) // max_images_per_row  # Calculate number of rows needed
+
+    for i in range(0, len(images), max_images_per_row):
+        cols_to_show = images[i:i + max_images_per_row]
+        
+        # Prepare to display in a grid format
+        cols = st.columns(max_images_per_row)  # Create columns dynamically
+        
+        for idx, img in enumerate(cols_to_show):
+            img = img.convert("RGB")  # Ensure the image is in RGB mode
+            
+            if idx < len(cols):
+                cols[idx].image(img, use_column_width=True)
+
+# Initialize your Streamlit app
+if app_mode == "Task 3":
+    st.write("## Task 3: Image Recognition 📸 and IR-Based Counting 📊")
+    
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of fruits, vegetables, or products for brand recognition and freshness detection", 
+                                      type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+    if uploaded_files:
+        st.write("### Uploaded Images:")
+        # Load the pre-trained YOLOv8 model
+        model = YOLO('yolov9c.pt')  # Adjust path to your YOLO model if needed
+
+        # Initialize a dictionary to store counts of detected products
+        product_count_dict = {}
+        circular_images = []
+        images=[]
+
+        for uploaded_file in uploaded_files:
+            img = Image.open(uploaded_file)
+            circular_img = make_image_circular(img)  # Create circular images
+            circular_images.append(circular_img)
+            images.append(img)
+
+        # Display the circular images in a matrix/grid format
+        display_images_in_grid(circular_images, max_images_per_row=4)
+
+        detected_images = []
+        
+        for idx, image in enumerate(images):
+            # Run object detection
+            results = model(image)
+
+            # Initialize counts for this image
+            image_counts = {}
+
+            # Display results with bounding boxes
+            for result in results:
+                img_with_boxes = result.plot()  # Get image with bounding boxes
+                detected_images.append(make_image_circular(image.resize((150, 150))))  # Resize and make circular
+                
+                # Display detected object counts per class
+                counts = result.boxes.cls.tolist()  # Extract class IDs
+                class_counts = {int(cls): counts.count(cls) for cls in set(counts)}
+                
+                # Update the image counts for this image
+                for cls_id, count in class_counts.items():
+                    product_name = result.names[cls_id]  # Get the product name from class ID
+                    image_counts[product_name] = count
+                
+                # Aggregate counts into the main product count dictionary
+                for product, count in image_counts.items():
+                    if product in product_count_dict:
+                        product_count_dict[product] += count
+                    else:
+                        product_count_dict[product] = count
+
+            # Option to visualize the bounding boxes on the image
+            if st.checkbox(f"Show image with highlighted boxes for image {idx + 1}", key=f"checkbox_{idx}"):
+                st.image(img_with_boxes, caption="Image with Highlighted Boxes", use_column_width=True)
+
+        # Display the total counts as a bar chart
+        st.write("### Total Product Counts Across All Images:")
+        if product_count_dict:
+            product_count_df = pd.DataFrame(product_count_dict.items(), columns=["Product", "Count"])
+            st.bar_chart(product_count_df.set_index("Product"))
+        else:
+            st.write("No products detected.")
+
+elif app_mode == "Task 4":
+    st.write("## Task 4: 🍏 Fruit and Vegetable Freshness Detector 🍅")
+    # Load the trained model
+    try:
+        model = tf.keras.models.load_model('fruit_freshness_model.h5')  # Using TensorFlow to load the model
+        st.success("Model loaded successfully!")
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+    
+    # File uploader for images (supports multiple files)
+    uploaded_files = st.file_uploader("Upload images of fruits/vegetables", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
+
+    if uploaded_files:
+        st.write("### Uploaded Images in Circular Format:")
+        circular_images = []
+        images=[]
+
+        for uploaded_file in uploaded_files:
+            img = Image.open(uploaded_file)
+            circular_img = make_image_circular(img)  # Create circular images
+            circular_images.append(circular_img)
+            images.append(img)
+
+        # Display the circular images in a matrix/grid format
+        display_images_in_grid(circular_images, max_images_per_row=4)
+
+        # Function to simulate loading process with a progress bar
+        def simulate_progress():
+            progress_bar = st.progress(0)
+            for percent_complete in range(100):
+                time.sleep(0.02)
+                progress_bar.progress(percent_complete + 1)
+
+        # Create an empty DataFrame to hold the image name and prediction results
+        results_df = pd.DataFrame(columns=["Image", "Prediction"])
+
+        # Create a dictionary to count the occurrences of each class
+        class_counts = {class_name: 0 for class_name in class_names}
+
+        # Button to initiate predictions
+        if st.button("Run Prediction"):
+            # Display progress bar
+            simulate_progress()
+
+            for idx, img in enumerate(images):  # Use circular images for predictions
+                img_array = preprocess_image(img.convert('RGB'))  # Convert to RGB
+
+                try:
+                    # Perform the prediction
+                    prediction = model.predict(img_array)
+
+                    # Get the class with the highest probability
+                    result = class_names[np.argmax(prediction)]
+                    st.success(f'Prediction for Image {idx + 1}: **{result}**')
+
+                    # Increment the class count
+                    class_counts[result] += 1
+
+                    # Add the result to the DataFrame
+                    result_data = pd.DataFrame({"Image": [uploaded_files[idx].name], "Prediction": [result]})
+                    results_df = pd.concat([results_df, result_data], ignore_index=True)
+
+                except Exception as e:
+                    st.error(f"Error occurred during prediction: {e}")
+
+            # Display class distribution as a bar chart
+            st.write("### Class Distribution:")
+            class_counts_df = pd.DataFrame(list(class_counts.items()), columns=['Class', 'Count'])
+            st.bar_chart(class_counts_df.set_index('Class'))
+
+            # Option to download the prediction results as a CSV file
+            st.write("### Download Results:")
+            csv = results_df.to_csv(index=False).encode('utf-8')
+            st.download_button(
+                label="Download prediction results as CSV",
+                data=csv,
+                file_name='prediction_results.csv',
+                mime='text/csv',
+            )
+
+            # Display the dataframe after the graph
+            st.write("### Prediction Data:")
+            st.dataframe(results_df)
+
+# Footer with animation
+st.markdown(""" 
+    <style>
+        @keyframes fade-in {
+            from { opacity: 0; }
+            to { opacity: 1;}
+        }
+        .footer {
+            text-align: center;
+            font-size: 1.1em;
+            animation: fade-in 2s;
+            padding-top: 2rem;
+        }
+    </style>
+    <div class="footer">
+        <p>© 2024 Amazon Smbhav Challenge. All rights reserved.</p>
+    </div>
+""", unsafe_allow_html=True)