app.py

from itertools import product
import streamlit as st
import numpy as np
import pandas as pd
from PIL import Image, ImageOps
import time
from paddleocr import PaddleOCR
import os
# from dotenv import load_dotenv
import torch
from transformers import AutoTokenizer, AutoModelForQuestionAnswering

# Load environment variables
# load_dotenv()
# huggingface_token = os.getenv("HF_TOKEN")

# Load TinyBERT model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic_tinybert")
model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic_tinybert")

# Initialize PaddleOCR
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": "anandimg.jpg"}
]

# Function to preprocess 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:
        img = image.resize((128, 128), Image.LANCZOS)
        img_array = np.array(img)

        if img_array.ndim == 2:  # Grayscale image
            img_array = np.stack([img_array] * 3, axis=-1)
        elif img_array.shape[2] == 1:  # Single-channel image
            img_array = np.concatenate([img_array, img_array, img_array], axis=-1)

        img_array = img_array / 255.0
        img_array = np.expand_dims(img_array, axis=0)

        return img_array
    except Exception as e:
        print(f"Error processing image: {e}")
        return None

# Function to perform Q&A with TinyBERT
def answer_question(context, question):
    """
    Extract the answer to a question from the given context using TinyBERT.
    Args:
        context (str): The text to search for answers.
        question (str): The question to answer.
    Returns:
        str: The extracted answer or an error message.
    """
    try:
        tokens = tokenizer.encode_plus(question, context, return_tensors="pt", truncation=True)
        input_ids = tokens["input_ids"]
        attention_mask = tokens["attention_mask"]

        # Perform question answering
        outputs = model(input_ids, attention_mask=attention_mask)
        start_scores = outputs.start_logits
        end_scores = outputs.end_logits

        answer_start = torch.argmax(start_scores)
        answer_end = torch.argmax(end_scores) + 1
        answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[0][answer_start:answer_end]))

        return answer
    except Exception as e:
        return f"Error: {e}"

# 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

    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"])
                st.image(img, use_column_width=True)
                st.write(member["name"])

# 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("Product Listing Assistant")

# 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", "Team Details", "Extract Product Details"])

if app_mode == "Welcome":
    st.write("# Welcome to the Product Listing Assistant! 🎉")
elif app_mode == "Project Details":
    st.write("""
    ## Project Overview:
    - Automates product listings from social media content.
    - Extracts product details from posts using OCR and Q&A.
    - Outputs structured, engaging, and optimized e-commerce listings.
    """)
elif app_mode == "Team Details":
    st.write("## Meet Our Team:")
    display_team_members(team_members)
elif app_mode == "Extract Product Details":
    st.write("## Extract Product Details Using OCR and Q&A")

    post_url = st.text_input("Enter Post URL:")
    uploaded_files = st.file_uploader("Upload Product Images", type=["jpeg", "png", "jpg"], accept_multiple_files=True)
    user_question = st.text_input("Ask a question about the extracted details:")

    if uploaded_files:
        st.write("### Uploaded Images:")
        simulate_progress()
        for uploaded_image in uploaded_files:
            image = Image.open(uploaded_image)
            st.image(image, use_column_width=True)
            simulate_progress()

            # Perform OCR
            st.write("Extracting text from image...")
            result = ocr.ocr(np.array(image), cls=True)
            extracted_text = " ".join([line[1][0] for line in result[0]])
            st.write("Extracted Text:")
            st.text(extracted_text)

            # Use Q&A model
            if user_question:
                st.write("### Answer to your question:")
                answer = answer_question(extracted_text, user_question)
                st.write(answer)