Update app.py

app.py

@@ -331,6 +331,7 @@ Claims Analysis:
 
     return completion.choices[0].message.content
 
+'''
 # Streamlit app
 def main():
     global data_extractor_url
@@ -423,4 +424,78 @@ def main():
             st.rerun()
 
 if __name__ == "__main__":
-    main()
+    main()
+'''
+
+# Initialize session state
+if 'messages' not in st.session_state:
+    st.session_state.messages = []
+
+def chatbot_response(user_input):
+    # Process the user input and generate a response
+    processing_level = ""
+    harmful_ingredient_analysis = ""
+    claims_analysis = ""
+    
+    if "analyze" in user_input.lower() and "http" in user_input.lower():
+        # Extract image URL from user input
+        image_url = user_input.split("http")[1].split()[0]
+        image_url = "http" + image_url
+
+        with st.spinner("Analyzing the product... This may take a moment."):
+            product_info_from_db = json.loads(extract_data_from_product_image([image_url], data_extractor_url))
+            
+            if product_info_from_db:
+                brand_name = product_info_from_db.get("brandName", "")
+                product_name = product_info_from_db.get("productName", "")
+                ingredients_list = [ingredient["name"] for ingredient in product_info_from_db.get("ingredients", [])]
+                claims_list = product_info_from_db.get("claims", [])
+
+                if len(ingredients_list) > 0:
+                    processing_level = analyze_processing_level(ingredients_list, brand_name, product_name, assistant1.id) if ingredients_list else ""
+                    harmful_ingredient_analysis = analyze_harmful_ingredients(ingredients_list, brand_name, product_name, assistant2.id) if ingredients_list else ""
+                if len(claims_list) > 0:                    
+                    claims_analysis = analyze_claims(claims_list, assistant3.id) if claims_list else ""
+                
+                final_analysis = generate_final_analysis(
+                    brand_name,
+                    product_name,
+                    processing_level,
+                    harmful_ingredient_analysis,
+                    claims_analysis
+                )
+                return final_analysis
+            else:
+                return "I'm sorry, I couldn't extract any information from that image. Could you try another image or provide more details about the product?"
+    else:
+        return "I'm here to analyze food products based on their image. Please provide an image URL and ask me to analyze it. For example: 'Can you analyze this product? http://example.com/image.jpg'"
+
+# Streamlit app
+st.title("Food Product Analysis Chatbot")
+
+st.write("Hello! I'm your food product analysis assistant. I can analyze food products based on their image. Just send me an image URL and ask me to analyze it.")
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# User input
+user_input = st.chat_input("Type your message here...")
+
+if user_input:
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": user_input})
+    with st.chat_message("user"):
+        st.markdown(user_input)
+
+    # Generate and display assistant response
+    response = chatbot_response(user_input)
+    st.session_state.messages.append({"role": "assistant", "content": response})
+    with st.chat_message("assistant"):
+        st.markdown(response)
+
+# Option to clear chat history
+if st.button("Clear Chat History"):
+    st.session_state.messages = []
+    st.rerun()