app.py

import streamlit as st
import torch
from transformers import LayoutLMv3Processor, LayoutLMv3ForTokenClassification
from PIL import Image
import io
import json
import pandas as pd
import plotly.express as px
import numpy as np
from typing import Dict, Any
import logging
import pytesseract
import re
from openai import OpenAI
import os
from dotenv import load_dotenv
from chatbot_utils import ask_receipt_chatbot
import time
from tensorboard.backend.event_processing import event_accumulator
from torch.utils.tensorboard import SummaryWriter
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load environment variables
load_dotenv()

# Initialize OpenAI client for Perplexity
api_key = os.getenv('PERPLEXITY_API_KEY')
if not api_key:
    st.error("""
    ⚠️ Perplexity API key not found! Please add your API key to the Space's secrets:
    1. Go to Space Settings
    2. Click on 'Repository secrets'
    3. Add a new secret with name 'PERPLEXITY_API_KEY'
    4. Add your Perplexity API key as the value
    """)
    st.stop()

client = OpenAI(
    api_key=api_key,
    base_url="https://api.perplexity.ai"
)

# Initialize LayoutLM model
@st.cache_resource
def load_model():
    model_name = "microsoft/layoutlmv3-base"
    processor = LayoutLMv3Processor.from_pretrained(model_name)
    model = LayoutLMv3ForTokenClassification.from_pretrained(model_name)
    return processor, model

def extract_json_from_llm_output(llm_result):
    match = re.search(r'\{.*\}', llm_result, re.DOTALL)
    if match:
        return match.group(0)
    return None

def extract_fields(image_path):
    # OCR
    text = pytesseract.image_to_string(Image.open(image_path))
    
    # Display OCR output for debugging
    st.subheader("Raw OCR Output")
    st.code(text)

    # Improved Regex patterns for fields
    patterns = {
        "name": r"Mrs\s+\w+\s+\w+",
        "date": r"Date[:\s]+([\d/]+)",
        "product": r"\d+\s+\w+.*Style\s+\d+",
        "amount_paid": r"Total Paid\s+\$?([\d.,]+)",
        "receipt_no": r"Receipt No\.?\s*:?\s*(\d+)"
    }

    results = {}
    for field, pattern in patterns.items():
        match = re.search(pattern, text, re.IGNORECASE)
        if match:
            results[field] = match.group(1) if match.groups() else match.group(0)
        else:
            results[field] = None

    return results

def extract_with_perplexity_llm(ocr_text):
    prompt = f"""
Extract the following fields from this receipt text:
- name
- date
- product
- amount_paid
- receipt_no

Text:
\"\"\"{ocr_text}\"\"\"

Return the result as a JSON object with those fields.
"""
    messages = [
        {
            "role": "system",
            "content": "You are an AI assistant that extracts structured information from text."
        },
        {
            "role": "user",
            "content": prompt
        }
    ]
    
    response = client.chat.completions.create(
        model="sonar-pro",
        messages=messages
    )
    return response.choices[0].message.content

def main():
    st.set_page_config(
        page_title="FormIQ - Intelligent Document Parser",
        page_icon="📄",
        layout="wide"
    )
    
    st.title("FormIQ: Intelligent Document Parser")
    st.markdown("""
    Upload your documents to extract and validate information using advanced AI models.
    """)
    
    # Sidebar
    with st.sidebar:
        st.header("Settings")
        document_type = st.selectbox(
            "Document Type",
            options=["invoice", "receipt", "form"],
            index=0
        )
        
        confidence_threshold = st.slider(
            "Confidence Threshold",
            min_value=0.0,
            max_value=1.0,
            value=0.5,
            step=0.05
        )
        
        st.markdown("---")
        st.markdown("### About")
        st.markdown("""
        FormIQ uses LayoutLMv3 and Perplexity AI to extract and validate information from documents.
        """)
    
    # Main content
    uploaded_file = st.file_uploader(
        "Upload Document",
        type=["png", "jpg", "jpeg", "pdf"],
        help="Upload a document image to process"
    )
    
    if uploaded_file is not None:
        # Display uploaded image
        image = Image.open(uploaded_file)
        st.image(image, caption="Uploaded Document", width=600)

        # Process button
        if st.button("Process Document"):
            with st.spinner("Processing document..."):
                try:
                    # Save the uploaded file to a temporary location
                    temp_path = "temp_uploaded_image.jpg"
                    image.save(temp_path)

                    # Extract fields using OCR + regex
                    fields = extract_fields(temp_path)

                    # Extract with Perplexity LLM
                    with st.spinner("Extracting structured data with Perplexity LLM..."):
                        try:
                            llm_result = extract_with_perplexity_llm(pytesseract.image_to_string(Image.open(temp_path)))
                            st.subheader("Structured Data (Perplexity LLM)")
                            st.code(llm_result, language="json")

                            # Display extracted fields
                            st.subheader("Extracted Fields")
                            fields_df = pd.DataFrame([fields])
                            st.dataframe(fields_df)

                        except Exception as e:
                            st.error(f"LLM extraction failed: {e}")

                except Exception as e:
                    logger.error(f"Error processing document: {str(e)}")
                    st.error(f"Error processing document: {str(e)}")

    st.markdown("---")
    st.header("💬 Receipt Chatbot")
    st.write("Ask questions about your receipts stored in DynamoDB.")
    user_question = st.text_input("Enter your question:", "What is the total amount paid?")
    if st.button("Ask Chatbot"):
        with st.spinner("Getting answer from Perplexity LLM..."):
            answer = ask_receipt_chatbot(user_question)
            st.success(answer)

    st.header("Model Training & Evaluation Demo")

    if st.button("Start Training"):
        epochs = 10
        num_classes = 3  # Example: 3 classes for confusion matrix
        losses = []
        val_losses = []
        accuracies = []
        progress = st.progress(0)
        chart = st.line_chart({"Loss": [], "Val Loss": [], "Accuracy": []})

        writer = SummaryWriter("logs")

        for epoch in range(epochs):
            # Simulate training
            loss = np.exp(-epoch/5) + np.random.rand() * 0.05
            val_loss = loss + np.random.rand() * 0.02
            acc = 1 - loss + np.random.rand() * 0.02
            losses.append(loss)
            val_losses.append(val_loss)
            accuracies.append(acc)
            chart.add_rows({"Loss": [loss], "Val Loss": [val_loss], "Accuracy": [acc]})
            progress.progress((epoch+1)/epochs)
            st.write(f"Epoch {epoch+1}: Loss={loss:.4f}, Val Loss={val_loss:.4f}, Accuracy={acc:.4f}")

            # Log to TensorBoard
            writer.add_scalar("loss", loss, epoch)
            writer.add_scalar("val_loss", val_loss, epoch)
            writer.add_scalar("accuracy", acc, epoch)

            # Simulate predictions and labels for confusion matrix
            y_true = np.random.randint(0, num_classes, 100)
            y_pred = y_true.copy()
            # Add some noise to predictions
            y_pred[np.random.choice(100, 10, replace=False)] = np.random.randint(0, num_classes, 10)
            cm = confusion_matrix(y_true, y_pred, labels=range(num_classes))

            # Plot and log confusion matrix as image
            fig, ax = plt.subplots()
            disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[f"Class {i}" for i in range(num_classes)])
            disp.plot(ax=ax)
            plt.close(fig)
            writer.add_figure("confusion_matrix", fig, epoch)

            time.sleep(0.5)

        writer.close()
        st.success("Training complete!")
        st.line_chart({"Loss": losses, "Val Loss": val_losses, "Accuracy": accuracies})

        # Show last confusion matrix in Streamlit
        st.subheader("Confusion Matrix (Last Epoch)")
        fig, ax = plt.subplots()
        disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=[f"Class {i}" for i in range(num_classes)])
        disp.plot(ax=ax)
        st.pyplot(fig)

    logdir = "logs"
    if os.path.exists(logdir) and os.listdir(logdir):
        ea = event_accumulator.EventAccumulator(logdir)
        ea.Reload()
        scalars = ea.Tags()['scalars']
        for tag in ['loss', 'val_loss', 'accuracy']:
            if tag in scalars:
                values = [s.value for s in ea.Scalars(tag)]
                st.line_chart({tag: values})
        # Show confusion matrix images if available
        if 'confusion_matrix' in ea.Tags()['images']:
            st.subheader("TensorBoard Confusion Matrices")
            for img in ea.Images('confusion_matrix'):
                st.image(img.encoded_image_string)
    else:
        st.info("No TensorBoard logs found. Please upload logs to the 'logs' directory.")

if __name__ == "__main__":
    main()