Updating to display requested error message

pages/FS_Model.py

@@ -182,6 +182,7 @@ def get_classification(client, deployment, user_input):
     )
     
     return completion.choices[0].message.content.strip()
+
 def process_input():
     try:
         current_model = st.session_state.selected_model
@@ -223,9 +224,10 @@ def process_input():
                     return response.choices[0].message.content.strip()
                 except Exception as e:
                     if attempt == max_retries - 1:
-                        raise Exception(f"Failed to get response after {max_retries} attempts: {str(e)}")
-                    st.warning(f"Attempt {attempt + 1} failed, retrying...")
+                        # Return user-friendly message instead of raising exception
+                        return "This question is not currently supported by the conversation agent or is being flagged by the AI algorithm as being outside its parameters. If you think the question should be answered, please inform the research team what should be added with justification and if available please provide links to resources to support further model training. Thank you."
                     time.sleep(1)
+        
 
         def perform_rag_query(input_text, conversation_history):
             try:
@@ -243,8 +245,69 @@ def process_input():
                 return safe_api_call(model_messages), relevant_docs
                 
             except Exception as e:
-                st.error(f"Error in RAG query: {str(e)}")
-                return "Lo siento, hubo un error al procesar tu consulta. Por favor, intenta nuevamente.", ""
+                # Use standardized error message
+                return "This question is not currently supported by the conversation agent or is being flagged by the AI algorithm as being outside its parameters. If you think the question should be answered, please inform the research team what should be added with justification and if available please provide links to resources to support further model training. Thank you.", ""
+        
+        # Update these sections too:
+        if model_config.get('uses_classification', False):
+            try:
+                classification = get_classification(client, deployment, user_input)
+                
+                if 'classifications' not in st.session_state:
+                    st.session_state.classifications = {}
+                st.session_state.classifications[len(st.session_state.messages[current_model]) - 1] = classification
+        
+                if classification == "0":
+                    initial_response, initial_docs = perform_rag_query(user_input, conversation_history)
+                else:
+                    model_messages = [
+                        {"role": "system", "content": PERSONA_PREFIX + EMOTIONAL_PROMPT}
+                    ] + st.session_state.messages[current_model]
+                    initial_response = safe_api_call(model_messages)
+                    
+            except Exception as e:
+                # Replace error message with standardized message
+                initial_response = "This question is not currently supported by the conversation agent or is being flagged by the AI algorithm as being outside its parameters. If you think the question should be answered, please inform the research team what should be added with justification and if available please provide links to resources to support further model training. Thank you."
+        
+        # And also update the RAG models section:
+        if model_config.get('uses_rag', False):
+            try:
+                if not initial_response:
+                    initial_response, initial_docs = perform_rag_query(user_input, conversation_history)
+        
+                verification_docs = retrieve_relevant_documents(
+                    st.session_state.vectorstore, 
+                    initial_response,
+                    conversation_history,
+                    client=client
+                )
+        
+                combined_docs = initial_docs + "\nContexto de verificación adicional:\n" + verification_docs
+        
+                verification_messages = [
+                    {
+                        "role": "system", 
+                        "content": f"Pregunta del paciente:{user_input} \nContexto: {combined_docs} \nRespuesta anterior: {initial_response}\n Verifique la precisión médica de la respuesta anterior y refine la respuesta según el contexto adicional."
+                    }
+                ]
+        
+                assistant_reply = safe_api_call(verification_messages)
+                
+            except Exception as e:
+                # Replace error message with standardized message
+                assistant_reply = "This question is not currently supported by the conversation agent or is being flagged by the AI algorithm as being outside its parameters. If you think the question should be answered, please inform the research team what should be added with justification and if available please provide links to resources to support further model training. Thank you."
+        else:
+            try:
+                model_messages = [
+                    {"role": "system", "content": model_config['prompt']}
+                ] + st.session_state.messages[current_model]
+                
+                assistant_reply = safe_api_call(model_messages)
+                
+            except Exception as e:
+                # Replace error message with standardized message
+                assistant_reply = "This question is not currently supported by the conversation agent or is being flagged by the AI algorithm as being outside its parameters. If you think the question should be answered, please inform the research team what should be added with justification and if available please provide links to resources to support further model training. Thank you."
+        
 
         initial_response = None
         initial_docs = ""
@@ -355,15 +418,20 @@ def check_document_relevance(query, doc, client):
         Documento: {doc}
         Respuesta:"""
     
-    response = client.chat.completions.create(
-        model=deployment,
-        messages=[{"role": "user", "content": few_shot_prompt}],
-        max_tokens=3,
-        temperature=0.1,
-        top_p=0.9
-    )
-    
-    return response.choices[0].message.content.strip().lower() == "sí"
+    try:
+        response = client.chat.completions.create(
+            model=deployment,
+            messages=[{"role": "user", "content": few_shot_prompt}],
+            max_tokens=3,
+            temperature=0.1,
+            top_p=0.9
+        )
+        
+        return response.choices[0].message.content.strip().lower() == "sí"
+    except Exception as e:
+        # In case of error, default to false (not relevant)
+        print(f"Error in relevance check: {str(e)}")
+        return False
 
 # In retrieve_relevant_documents function
 def retrieve_relevant_documents(vectorstore, query, conversation_history, client, top_k=3, score_threshold=0.5):