Create checkpointapp.py

checkpointapp.py

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+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()