Issue fix

app.py

@@ -15,6 +15,7 @@ from huggingface_hub import hf_hub_download
 from langchain_openai import OpenAIEmbeddings, ChatOpenAI
 from SkinCancerDiagnosis import initialize_classifier
 from rag_pipeline import invoke_rag_chain
+from langchain_core.messages import HumanMessage, AIMessage
 
 nest_asyncio.apply()
 device='cuda' if torch.cuda.is_available() else 'cpu'
@@ -135,15 +136,30 @@ if prompt := st.chat_input("Ask a follow-up question..."):
 
     with st.chat_message("assistant"):
         with st.spinner("Thinking..."):
-            if len(st.session_state.messages) > 1:
-                response = llm.invoke([{"role": m["role"], "content": m["content"]} for m in st.session_state.messages])
-            else:
-                response = rag_chain.invoke(prompt)
-                response = response['result']
-
-            st.markdown(response)
-            st.session_state.messages.append({"role": "assistant", "content": response})
-
+            # Convert messages to LangChain format
+            chat_history = []
+            for msg in st.session_state.messages[:-1]:  # Exclude the current prompt
+                if msg["role"] == "user":
+                    chat_history.append(HumanMessage(content=msg["content"]))
+                else:
+                    chat_history.append(AIMessage(content=msg["content"]))
+
+            # Get response
+            response = llm.invoke([HumanMessage(content=prompt)] + chat_history)
+            assistant_response = response.content
+
+            st.markdown(assistant_response)
+            st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+        # with st.spinner("Thinking..."):
+        #     if len(st.session_state.messages) > 1:
+        #         response = llm.invoke([{"role": m["role"], "content": m["content"]} for m in st.session_state.messages])
+        #     else:
+        #         response = rag_chain.invoke(prompt)
+        #         response = response['result']
+        #
+        #     st.markdown(response)
+        #     st.session_state.messages.append({"role": "assistant", "content": response})
+        #
 
 if st.session_state.messages and st.button("📄 Download Chat as PDF"):
     pdf_file = export_chat_to_pdf(st.session_state.messages)

requirements.txt

@@ -16,4 +16,5 @@ langchain_openai
 nest_asyncio
 sentence_transformers
 langchain-qdrant
-huggingface_hub
+huggingface_hub
+langchain_core