from itertools import product
import streamlit as st
import numpy as np
import pandas as pd
from PIL import Image, ImageOps, ImageDraw, ImageFont
import time
from paddleocr import PaddleOCR, draw_ocr
import os
from dotenv import load_dotenv
import os
from huggingface_hub import login
load_dotenv() # Load .env file
huggingface_token = os.getenv("HF_TOKEN")
login(huggingface_token)
##########################LLAMA3BI################################
from huggingface_hub import InferenceClient
client = InferenceClient(api_key=huggingface_token)
messages = [
{"role": "system", "content": """Your task is to get the product details out of the text given.
The text given will be raw text from OCR of social media images of products,
and the goal is to get product details and description so that it can be used for amazon product listing.
TRY TO KEEP THE LISTING IN FOLLOWING FORMAT.
📦 [Product Name]
💰 Price: $XX.XX
✨ Key Features:
• [Main Feature 1]
• [Main Feature 2]
• [Main Feature 3]
📸 [Product Image]
🏷 Available Now on Amazon
✈️ Prime Shipping Available
🛍 Shop Now: [Link]
🔍 Search: [Main Keywords]
[#RelevantHashtags] """},
]
# Function to get Instagram post details
import instaloader
from io import BytesIO
import requests
def get_instagram_post_details(post_url):
try:
# Initialize Instaloader
L = instaloader.Instaloader()
# Extract shortcode from URL
shortcode = post_url.split('/')[-2]
# Load post using Instaloader
post = instaloader.Post.from_shortcode(L.context, shortcode)
# Retrieve caption
caption = post.caption
# Retrieve the image URL
image_url = post.url
# Fetch image using requests
response = requests.get(image_url)
response.raise_for_status() # Raise an exception for failed requests
# Open image using PIL and convert to NumPy array
img = Image.open(BytesIO(response.content))
img_array = np.array(img)
return caption, img_array
except Exception as e:
return str(e), None
# Initialize PaddleOCR model
ocr = PaddleOCR(use_angle_cls=True, lang='en')
# Team details
team_members = [
{"name": "Aman Deep", "image": "aman.jpg"}, # Replace with actual paths to images
{"name": "Nandini", "image": "nandini.jpg"},
{"name": "Abhay Sharma", "image": "abhay.jpg"},
{"name": "Ratan Prakash Mishra", "image": "ratan.jpg"}
]
# Function to preprocess the images for the model
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 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
circular_img = img
st.image(circular_img, use_column_width=True) # Display the circular image
st.write(member["name"]) # Display the name below the image
# 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)
# 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: sadhya")
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("""
### Problem Statement
_Develop a system that automates Amazon product listings from social media content, extracting and organizing details from posts to generate accurate, engaging, and optimized listings._
---
### Solution Overview
Our system simplifies the listing process by analyzing social media content, using OCR, image recognition, LLMs, and internet data to create professional Amazon listings.
---
### Task Breakdown
#### Task 1: OCR for Image and Label Details
**Objective:** Extract core product details from images, labels, and packaging found in social media posts.
- **Tools:** PaddleOCR, LLMs.
- **Approach:**
- Use PaddleOCR to scan images for text, identifying product names, brands, and key features.
- Apply LLMs to refine extracted data, categorize key information (product name, type, features), and enhance product descriptions.
- Integrate internet sources to cross-verify product details, retrieve additional information, and collect metadata like the brand background or product specs.
---
#### Additional Task: Image Recognition & Object Counting
**Objective:** Quantify objects within social media images for batch products or multi-item listings.
- **Tools:** YOLOv8.
- **Approach:**
- Train YOLOv8 on a relevant dataset to recognize specific product types or packaging layouts.
- Use object detection counts to provide quantitative data (e.g., "3-item bundle"), enhancing accuracy in listings.
---
#### Task 2: Data Validation & Structuring
**Objective:** Organize and validate extracted information, ensuring it’s formatted to meet Amazon’s listing requirements.
- **Tools:** Regex, LLMs.
- **Approach:**
- Format and validate extracted details into Amazon-compliant structures (titles, descriptions, bullet points).
- Use regex and parser tools for accuracy checks.
- Leverage LLMs to create compelling descriptions and marketing brochures.
- Search online for supplementary media (images/videos) to enrich the listing.
---
#### Task 3: Amazon API Integration
**Objective:** Connect with Amazon’s API to publish fully formed product listings directly.
- **Tools:** Amazon MWS or Selling Partner API.
- **Approach:**
- Send structured listing data (text, media, product details) to Amazon’s API endpoints.
- Handle feedback for submission errors and make necessary adjustments.
- Develop a UI/dashboard for users to preview and edit listings before publishing.
---
### Future Enhancements
- **Model Improvement:** Further refine OCR and parsing accuracy.
- **Dashboard Development:** Enable users to preview and customize listings.
- **Multi-Market Compatibility:** Expand support to other e-commerce platforms.
This approach automates listing creation directly from social media content, helping sellers quickly launch optimized Amazon product pages.
""")
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.")
# Instantiate Instaloader
L = instaloader.Instaloader()
# Streamlit UI
st.title("Instagram Post Details Extractor")
# Text input for Instagram post URL
post_url = st.text_input("Enter Instagram Post URL:")
if post_url:
caption, imageArray = get_instagram_post_details(post_url)
if caption or imageArray.shape[0] > 0:
st.subheader("Caption:")
st.write(caption)
st.subheader("Image:")
st.image(imageArray, use_column_width=True)
# Convert image to numpy array for OCR processing
img_array = imageArray
#############################
# Perform OCR on the image
result = ocr.ocr(img_array, cls=True)
text = ""
for line in result:
for box in line:
currText, confidence = box[1][0], box[1][1]
text += currText + " "
message = f"image ocr: {text} Caption: {caption}"
st.write(message)
#OCR result text to be parsed here through LLM and get product listing content.
simulate_progress()
messages.append({"role": "user", "content": message})
completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=messages,
max_tokens=500
)
productListingContent = completion.choices[0].message
st.markdown(productListingContent.content)
#############################
else:
st.error("Failed to retrieve the post details. Please check the URL. ////////")
# 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_images.append(img)
# Display the circular images in a matrix/grid format
display_images_in_grid(circular_images, max_images_per_row=4)
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)
# 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)
text = ""
for line in result:
for box in line:
currText, confidence = box[1][0], box[1][1]
text += currText + " "
st.write(f"OCR Result: {text}")
#OCR result text to be parsed here through LLM and get product listing content.
simulate_progress()
messages.append({"role": "user", "content": text})
completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-3B-Instruct",
messages=messages,
max_tokens=500
)
productListingContent = completion.choices[0].message
st.markdown(productListingContent.content)
st.markdown("---")
#########################################
else:
st.write("Please upload images to extract product details.")
# Footer with animation
st.markdown("""
""", unsafe_allow_html=True)