Update app.py

app.py

@@ -8,6 +8,8 @@ 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
@@ -32,7 +34,7 @@ class SmartShoppingAssistant:
         matches = get_close_matches(
             product_name.upper(),
             self.df[self.df['IsAvailable'] == "Yes"]['ProductName'].str.upper().tolist(),
-            n=5,  # Get more matches
+            n=5,
             cutoff=threshold
         )
         return matches if matches else []
@@ -58,6 +60,28 @@ class SmartShoppingAssistant:
     
     def process_query(self, query):
         return self.agent.run(f"Find the best matches for: {query}")
+    
+    def extract_text_from_image(self, image):
+        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):
+        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)}"
 
 def generate_receipt(cart_items):
     pdf = FPDF()
@@ -87,6 +111,21 @@ def main():
     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'):
+                    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
+            except Exception as e:
+                st.error(f"Error processing file: {str(e)}")
+    
     if 'cart' not in st.session_state:
         st.session_state.cart = []