Update app.py

app.py

@@ -4,83 +4,34 @@ from dotenv import load_dotenv
 load_dotenv()
 
 from langchain.agents.openai_assistant import OpenAIAssistantRunnable
+from langchain.agents import AgentExecutor
+
 from langchain.schema import HumanMessage, AIMessage
 
-import gradio as gr
+import gradio
 
-# Load API key and assistant IDs
 api_key = os.getenv('OPENAI_API_KEY')
-extractor_agents = {
-    "Solution Specifier A": os.getenv('ASSISTANT_ID_SOLUTION_SPECIFIER_A'),
-    "Solution Specifier A1": os.getenv('ASSISTANT_ID_SOLUTION_SPECIFIER_A1'),
-}
+extractor_agent = os.getenv('ASSISTANT_ID_SOLUTION_SPECIFIER_A')
 
-# Function to create a new extractor LLM instance
-def get_extractor_llm(agent_id):
-    return OpenAIAssistantRunnable(assistant_id=agent_id, api_key=api_key, as_agent=True)
+extractor_llm = OpenAIAssistantRunnable(assistant_id=extractor_agent, api_key=api_key, as_agent=True)
 
-# Utility function to remove citations
 def remove_citation(text):
     # Define the regex pattern to match the citation format 【number†text】
     pattern = r"【\d+†\w+】"
     # Replace the pattern with an empty string
     return re.sub(pattern, "📚", text)
 
-# Prediction function
-def predict(message, history, selected_agent):
-    # Get the extractor LLM for the selected agent
-    agent_id = extractor_agents[selected_agent]
-    extractor_llm = get_extractor_llm(agent_id)
-    
-    # Prepare the chat history
+def predict(message, history):
     history_langchain_format = []
     for human, ai in history:
         history_langchain_format.append(HumanMessage(content=human))
         history_langchain_format.append(AIMessage(content=ai))
     history_langchain_format.append(HumanMessage(content=message))
-    
-    # Get the response
     gpt_response = extractor_llm.invoke({"content": message})
     output = gpt_response.return_values["output"]
     non_cited_output = remove_citation(output)
     return non_cited_output
 
-# Define the Gradio interface using Blocks for layout
-def app_interface():
-    with gr.Blocks(
-        css="""
-        .container {
-            max-width: 100% !important;
-            padding: 0 !important;
-        }
-        .main-div {
-            height: 100vh;
-            display: flex;
-            flex-direction: column;
-        }
-        .chat-div {
-            flex-grow: 1;
-            min-height: 0;
-        }
-        """
-    ) as demo:
-        with gr.Column(elem_classes="main-div"):
-            dropdown = gr.Dropdown(
-                choices=list(extractor_agents.keys()),
-                value="Solution Specifier A",
-                label="Choose Extractor Agent",
-                interactive=True,
-                container=True
-            )
-            chat = gr.ChatInterface(
-                fn=predict,
-                additional_inputs=[dropdown],
-                title="Solution Specifier Chat",
-                description="Test with different solution specifiers",
-                elem_classes="chat-div"
-            )
-    return demo
-
-# Launch the app
-chat_interface = app_interface()
-chat_interface.launch(share=True)
+#gradio.Markdown("Click [here](https://www.google.com) to visit Google.")
+chat = gradio.ChatInterface(predict, title="Solution Specifier A", description="testing for the time being")
+chat.launch(share=True)