Update app.py

app.py

@@ -16,6 +16,19 @@ import openai
 import multiprocessing
 import autogen.agentchat.user_proxy_agent as upa
 
+
+config_list = [
+    {
+        "model": "gpt-4",
+        "api_key": st.secrets["OPENAI_API_KEY"]
+    }
+]
+
+gpt4_api_key = config_list[0]["api_key"]
+os.environ['OPENAI_API_KEY'] = st.secrets["OPENAI_API_KEY"]
+openai.api_key = st.secrets["OPENAI_API_KEY"]
+
+
 class OutputCapture:
     def __init__(self):
         self.contents = []
@@ -43,17 +56,6 @@ class ExtendedUserProxyAgent(upa.UserProxyAgent):
         self.log_interaction(f"Human input: {human_input}")
         return human_input
     
-# Example usage:
-config_list = [
-    {
-        "model": "gpt-4",
-        "api_key": st.secrets["OPENAI_API_KEY"]
-    }
-]
-
-gpt4_api_key = config_list[0]["api_key"]
-os.environ['OPENAI_API_KEY'] = st.secrets["OPENAI_API_KEY"]
-openai.api_key = st.secrets["OPENAI_API_KEY"]
 
 def build_vector_store(pdf_path, chunk_size=1000):
     loaders = [PyPDFLoader(pdf_path)]
@@ -85,53 +87,57 @@ def answer_question(question, qa_chain):
     response = qa_chain({"question": question})
     return response["answer"]
 
-def setup_agents(config_list, answer_function):
+def initiate_task(user_proxy, assistant, user_question):
+    user_proxy.initiate_chat(
+        assistant,
+        message= user_question
+            )
+    
+def initiate_task_process(queue, tmp_path, user_question):
+    vectorstore = build_vector_store(tmp_path)
+    qa = setup_qa_chain(vectorstore)
+    def answer_question(question):
+        response = qa({"question": question})
+        return response["answer"]
     llm_config={
-        "request_timeout": 600,
-        "seed": 42,
-        "config_list": config_list,
-        "temperature": 0,
-        "functions": [
-            {
-                "name": "answer_question",
-                "description": "Answer any questions relate to the paper",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "question": {
-                            "type": "string",
-                            "description": "The question to ask in relation to the paper",
-                        }
-                    },
-                    "required": ["question"],
+    "request_timeout": 600,
+    "seed": 42,
+    "config_list": config_list,
+    "temperature": 0,
+    "functions": [
+        {
+            "name": "answer_question",
+            "description": "Answer any questions in relation to the paper",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "question": {
+                        "type": "string",
+                        "description": "The question to ask in relation to the paper",
+                    }
                 },
-            }
-        ],
+                "required": ["question"],
+            },
+        }
+    ],
     }
-    assistant = autogen.AssistantAgent(name="assistant", llm_config=llm_config)
-    user_proxy = ExtendedUserProxyAgent(
+
+    # create an AssistantAgent instance named "assistant"
+    assistant = autogen.AssistantAgent(
+        name="assistant",
+        llm_config=llm_config,
+    )
+    # create a UserProxyAgent instance named "user_proxy"
+    user_proxy = autogen.UserProxyAgent(
         name="user_proxy",
         human_input_mode="NEVER",
         max_consecutive_auto_reply=10,
         code_execution_config={"work_dir": "."},
         llm_config=llm_config,
         system_message="""Reply TERMINATE if the task has been solved at full satisfaction.
-Otherwise, reply CONTINUE, or the reason why the task is not solved yet.""",
+    Otherwise, reply CONTINUE, or the reason why the task is not solved yet.""",
         function_map={"answer_question": answer_question}
     )
-    return assistant, user_proxy
-
-def initiate_task(user_proxy, assistant, user_question):
-    user_proxy.initiate_chat(
-        assistant,
-        message= user_question
-            )
-    
-def initiate_task_process(queue, tmp_path, user_question):
-    loaders = [PyPDFLoader(tmp_path)]
-    vectorstore = build_vector_store(tmp_path)
-    qa_chain = setup_qa_chain(vectorstore)
-    assistant, user_proxy = setup_agents(config_list, lambda q: answer_uniswap_question(q, qa_chain))
 
     output_capture = OutputCapture()
     sys.stdout = output_capture