checkpointapp.py

import streamlit as st
import pandas as pd
from langchain.memory import ConversationBufferMemory
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
from PIL import Image
import PyPDF2
import os
from langchain.agents import initialize_agent, Tool
from langchain.agents.agent_types import AgentType
from difflib import get_close_matches
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Configure Google API
genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))

def configure_gemini(api_key):
    genai.configure(api_key=api_key)
    return genai.GenerativeModel('gemini-2.0-flash-thinking-exp')

model = configure_gemini(os.environ['GOOGLE_API_KEY'])
            
# Initialize Gemini models
llm_flash_exp = ChatGoogleGenerativeAI(
    model="gemini-2.0-flash-exp",
    max_retries=2
)

class SmartShoppingAssistant:
    def __init__(self, products_df):
        self.df = products_df
        self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
        self.setup_agent()
    
    def find_closest_product(self, product_name, threshold=0.6):
        matches = get_close_matches(
            product_name.upper(),
            self.df['ProductName'].str.upper().tolist(),
            n=3,  # Increased to get more potential matches
            cutoff=threshold
        )
        return matches if matches else []
    
    def match_products_with_catalogue(self, extracted_items):
        """Match extracted items with catalogue products using Gemini"""
        product_list = self.df['ProductName'].tolist()
        product_string = ", ".join(product_list)
        
        prompt = f"""
        Given these extracted items and quantities:
        {extracted_items}
        
        And this product catalogue:
        {product_string}

        Match each item with the most appropriate product from the catalogue.
        For each item, provide:
        1. The exact product name from the catalogue
        2. The quantity (if specified, otherwise assume 1)
        3. Any specific requirements (brand, size, etc.)

        Format the response as:
        ProductName == "MATCHED_PRODUCT" quantity: NUMBER or ProductName == "MATCHED_PRODUCT" quantity: NUMBER

        Only include products that have good matches in the catalogue.
        """
        
        try:
            matches = llm_flash_exp.predict(prompt)
            return matches.strip()
        except Exception as e:
            return f"Error matching products: {str(e)}"
    
    def search_products_fuzzy(self, product_names_with_quantities):
        """Search for products using fuzzy matching with quantity information"""
        results = pd.DataFrame()
        for item in product_names_with_quantities:
            product_info = item.split('quantity:')
            product_name = product_info[0].strip()
            quantity = int(product_info[1].strip()) if len(product_info) > 1 else 1
            
            # Clean up product name
            if 'ProductName ==' in product_name:
                product_name = product_name.split('==')[1].strip(' "\'')
            
            closest_matches = self.find_closest_product(product_name)
            for match in closest_matches:
                matched_products = self.df[self.df['ProductName'].str.upper() == match.upper()]
                if not matched_products.empty:
                    matched_products['Quantity'] = quantity
                    results = pd.concat([results, matched_products])
                    break
        
        return results
        
    def setup_agent(self):
        """Set up the LangChain agent with necessary tools"""
        def search_products(query):
            try:
                # Split into individual product entries
                product_entries = [entry.strip() for entry in query.split('or')]
                
                results = self.search_products_fuzzy(product_entries)
                if not results.empty:
                    # Format results with quantity
                    formatted_results = results.apply(
                        lambda x: f"{x['ProductName']} (Quantity: {x['Quantity']})\nPrice: ${x['RetailPrice']:.2f}\n",
                        axis=1
                    )
                    return "\n".join(formatted_results)
                return "No products found matching your criteria."
            except Exception as e:
                return f"Error executing query: {str(e)}"
        
        tools = [
            Tool(
                name="Product Search",
                func=search_products,
                description="Search for products in the supermarket database using fuzzy matching"
            )
        ]
        
        self.agent = initialize_agent(
            tools=tools,
            memory=self.memory,
            llm=llm_flash_exp,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=True,
            max_iterations=3
        )
    
    def process_natural_language_query(self, query):
        """Process natural language query with two-step matching"""
        try:
            # First step: Extract items and quantities
            extraction_prompt = f"""
            Extract the products and their quantities from this shopping request.
            Include any specific requirements mentioned.
            
            Shopping request: {query}
            
            Format each item with its quantity (assume 1 if not specified).
            """
            
            extracted_items = llm_flash_exp.predict(extraction_prompt)
            
            # Second step: Match with catalogue
            matched_products = self.match_products_with_catalogue(extracted_items)
            
            # Third step: Search and return results
            result = self.agent.run(f"Search for products matching the specified names: {matched_products}")
            return result
        except Exception as e:
            return f"Error processing query: {str(e)}"
    
    def extract_text_from_image(self, image):
        """Extract text from uploaded image using Gemini"""
        prompt = """
        Analyze this image and extract products and their quantities.
        If quantities aren't specified, make reasonable assumptions based on typical shopping patterns.
        List each item with its quantity.
        """
        try:
            response = model.generate_content([prompt, image])
            return response.text
        except Exception as e:
            return f"Error processing image: {str(e)}"
    
    def extract_text_from_pdf(self, pdf_file):
        """Extract text from uploaded PDF"""
        try:
            pdf_reader = PyPDF2.PdfReader(pdf_file)
            text = ""
            for page in pdf_reader.pages:
                text += page.extract_text()
            return text
        except Exception as e:
            return f"Error processing PDF: {str(e)}"

# Main function remains the same
def main():
    st.set_page_config(page_title="Smart Shopping Assistant", layout="wide")
    st.title("🛒 Smart Shopping Assistant")
    
    @st.cache_data
    def load_product_data():
        return pd.read_csv('supermarket4.csv')
    
    df = load_product_data()
    assistant = SmartShoppingAssistant(df)
    
    with st.sidebar:
        st.header("Upload Shopping List")
        uploaded_file = st.file_uploader(
            "Upload an image or PDF of your shopping list",
            type=['png', 'jpg', 'jpeg', 'pdf']
        )
        
        if uploaded_file:
            try:
                if uploaded_file.type.startswith('image'):
                    with st.spinner("Extracting items from image..."):
                        image = Image.open(uploaded_file)
                        extracted_text = assistant.extract_text_from_image(image)
                        st.session_state.query = extracted_text
                elif uploaded_file.type == 'application/pdf':
                    with st.spinner("Extracting items from PDF..."):
                        extracted_text = assistant.extract_text_from_pdf(uploaded_file)
                        st.session_state.query = extracted_text
            except Exception as e:
                st.error(f"Error processing file: {str(e)}")

    col1, col2 = st.columns([2, 1])
    
    with col1:
        st.header("Search Products")
        query = st.text_area(
            "Describe what you're looking for (include quantities if needed):",
            height=100,
            placeholder="Example: 2 boxes of healthy breakfast cereals under $5, 1 gallon of milk",
            value=st.session_state.get('query', '')
        )
        
        if st.button("Search"):
            if query:
                with st.spinner("Searching for products..."):
                    results = assistant.process_natural_language_query(query)
                    st.write("### Results")
                    st.write(results)
            else:
                st.warning("Please enter a search query or upload a shopping list.")
    
    with col2:
        st.header("Shopping Cart")
        if 'cart' not in st.session_state:
            st.session_state.cart = []
        st.write("Your cart is empty" if not st.session_state.cart else "Cart items here")

if __name__ == "__main__":
    main()