Update app.py

app.py

@@ -1,20 +1,20 @@
 import streamlit as st
 import pandas as pd
-import google.generativeai as genai
 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
-from fpdf import FPDF
-from PIL import Image
-import PyPDF2
-import os
 
 # Load environment variables
 load_dotenv()
 
+# Configure Google API
 genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
 
 def configure_gemini(api_key):
@@ -22,7 +22,12 @@ def configure_gemini(api_key):
     return genai.GenerativeModel('gemini-2.0-flash-thinking-exp')
 
 model = configure_gemini(os.environ['GOOGLE_API_KEY'])
-llm_flash_exp = ChatGoogleGenerativeAI(model="gemini-2.0-flash-exp", max_retries=2)
+            
+# Initialize Gemini models
+llm_flash_exp = ChatGoogleGenerativeAI(
+    model="gemini-2.0-flash-exp",
+    max_retries=2
+)
 
 class SmartShoppingAssistant:
     def __init__(self, products_df):
@@ -33,35 +38,126 @@ class SmartShoppingAssistant:
     def find_closest_product(self, product_name, threshold=0.6):
         matches = get_close_matches(
             product_name.upper(),
-            self.df[self.df['IsAvailable'] == "Yes"]['ProductName'].str.upper().tolist(),
-            n=5,
+            self.df['ProductName'].str.upper().tolist(),
+            n=3,  # Increased to get more potential matches
             cutoff=threshold
         )
         return matches if matches else []
     
-    def search_products_fuzzy(self, query):
-        results = []
-        closest_matches = self.find_closest_product(query)
-        for match in closest_matches:
-            matched_products = self.df[self.df['ProductName'].str.upper() == match.upper()]
-            if not matched_products.empty:
-                results.append(matched_products.iloc[0].to_dict())
-        return results
+    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):
-            results = self.search_products_fuzzy(query)
-            if results:
-                return "\n".join([f"{res['ProductName']} - ${res['RetailPrice']}" for res in results])
-            return "No products found."
+            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")]
-        self.agent = initialize_agent(tools=tools, memory=self.memory, llm=llm_flash_exp, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
+        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_query(self, query):
-        return self.agent.run(f"Find the best matches for: {query}")
+    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.
@@ -74,6 +170,7 @@ class SmartShoppingAssistant:
             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 = ""
@@ -83,83 +180,64 @@ class SmartShoppingAssistant:
         except Exception as e:
             return f"Error processing PDF: {str(e)}"
 
-def generate_receipt(cart_items):
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, txt="Supermarket Receipt", ln=True, align='C')
-    pdf.ln(10)
-    total = 0
-    for item in cart_items:
-        line = f"{item['ProductName']} - ${item['RetailPrice']}"
-        pdf.cell(200, 10, txt=line, ln=True)
-        total += item['RetailPrice']
-    pdf.ln(10)
-    pdf.cell(200, 10, txt=f"Total: ${total:.2f}", ln=True)
-    receipt_path = "receipt.pdf"
-    pdf.output(receipt_path)
-    return receipt_path
-
+# 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('supermarket4i.csv')
+        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'])
+        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'):
-                    image = Image.open(uploaded_file)
-                    extracted_text = assistant.extract_text_from_image(image)
-                    st.session_state.query = extracted_text
+                    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':
-                    extracted_text = assistant.extract_text_from_pdf(uploaded_file)
-                    st.session_state.query = extracted_text
+                    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])
     
-    if 'cart' not in st.session_state:
-        st.session_state.cart = []
-    
-    query = st.text_input("Search for a product:")
-    if st.button("Search"):
-        if query:
-            results = assistant.search_products_fuzzy(query)
-            if results:
-                for res in results:
-                    col1, col2 = st.columns([3, 1])
-                    with col1:
-                        st.write(f"**{res['ProductName']}** - ${res['RetailPrice']}")
-                    with col2:
-                        if st.button("Add to Cart", key=res['ProductName']):
-                            st.session_state.cart.append(res)
-                            st.rerun()
+    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("No matching products found.")
+                st.warning("Please enter a search query or upload a shopping list.")
     
-    st.header("Shopping Cart")
-    if st.session_state.cart:
-        total = sum(item['RetailPrice'] for item in st.session_state.cart)
-        for item in st.session_state.cart:
-            st.write(f"{item['ProductName']} - ${item['RetailPrice']}")
-        st.write(f"**Total: ${total:.2f}**")
-        
-        if st.button("Checkout"):
-            receipt_path = generate_receipt(st.session_state.cart)
-            with open(receipt_path, "rb") as file:
-                st.download_button(label="Download Receipt", data=file, file_name="receipt.pdf", mime="application/pdf")
+    with col2:
+        st.header("Shopping Cart")
+        if 'cart' not in st.session_state:
             st.session_state.cart = []
-            st.rerun()
-    else:
-        st.write("Your cart is empty.")
+        st.write("Your cart is empty" if not st.session_state.cart else "Cart items here")
 
 if __name__ == "__main__":
-    main()
+    main()