Update app.py

app.py

@@ -1,99 +1,128 @@
-from dotenv import load_dotenv
 import os
+import gradio as gr
+from dotenv import load_dotenv
+
 from langchain_openai import ChatOpenAI, OpenAIEmbeddings
 from langchain_astradb import AstraDBVectorStore
-from langchain.agents import initialize_agent, AgentType
-from langchain.tools.retriever import create_retriever_tool
+
 from langchain.agents import AgentExecutor
-from github import fetch_github_issues
-import gradio as gr
+from langchain.agents.openai_functions_agent import (
+    create_openai_functions_agent,
+)
+from langchain.tools.retriever import create_retriever_tool
 from langchain import hub
+
+from github import fetch_github_issues
 from note import note_tool
 
+# --------------------------------------------------
+# ENV
+# --------------------------------------------------
 load_dotenv()
 
-def connet_to_vstore():
+# --------------------------------------------------
+# VECTOR STORE
+# --------------------------------------------------
+def connect_to_vstore():
     embeddings = OpenAIEmbeddings()
-    desire_namespace = os.getenv("ASTRA_DB_KEYSPACE")
-    ASTRA_DB_APPLICATION_TOKEN = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
-    ASTRA_DB_API_ENDPOINT = os.getenv("ASTRA_DB_API_ENDPOINT")  
-    if desire_namespace:
-        ASTRA_DB_KEYSPACE = desire_namespace
-    else:
-        ASTRA_DB_KEYSPACE = None
-    
-    vstore = AstraDBVectorStore(
+
+    return AstraDBVectorStore(
         embedding=embeddings,
         collection_name="github",
-        namespace=ASTRA_DB_KEYSPACE,
-        api_endpoint=ASTRA_DB_API_ENDPOINT,
-        token=ASTRA_DB_APPLICATION_TOKEN,
+        namespace=os.getenv("ASTRA_DB_KEYSPACE"),
+        api_endpoint=os.getenv("ASTRA_DB_API_ENDPOINT"),
+        token=os.getenv("ASTRA_DB_APPLICATION_TOKEN"),
     )
-    return vstore
 
-vstore = connet_to_vstore()
-add_to_vectorstore = input("Do you want to update the issue? (yes/no): ").lower() in ["yes", "y"]
-if add_to_vectorstore:
+
+vstore = connect_to_vstore()
+
+# --------------------------------------------------
+# OPTIONAL: UPDATE VECTOR STORE
+# (disable input() for Spaces)
+# --------------------------------------------------
+UPDATE_VECTORSTORE = True  # change to False if not needed
+
+if UPDATE_VECTORSTORE:
     owner = "Ini-design"
     repo = "register"
     issues = fetch_github_issues(owner, repo)
+
     try:
         vstore.delete_collection()
-    except:
+    except Exception:
         pass
-        
-    vstore = connet_to_vstore()
+
+    vstore = connect_to_vstore()
     vstore.add_documents(issues)
-    
-   # results = vstore.similarity_search('flash message', k=3)
-   # for res in results:
-   #     print(f"*{res.page_content} {res.metadata}")
-   
-retriever = vstore.as_retriever(search_kwargs={"k":3})
+
+# --------------------------------------------------
+# RETRIEVER TOOL
+# --------------------------------------------------
+retriever = vstore.as_retriever(search_kwargs={"k": 3})
+
 retriever_tool = create_retriever_tool(
     retriever,
-    "github_search",
-    "search for information aabout github issues. For any question abour github issue, you must used this tools!"
+    name="github_search",
+    description=(
+        "Search for information about GitHub issues. "
+        "Use this tool for any GitHub issue-related questions."
+    ),
 )
 
-prompt = hub.pull("hwchase17/openai-functions_agent")
-llm = ChatOpenAI()
+tools = [retriever_tool, note_tool]
 
-tools = [retriever_tool], note_tool
-agent = initialize_agent(llm, tools, prompt)
-agent_executor = AgentExecutor(agent=AgentType.OPENAI_FUNCTIONS, tools=tools, verbose=True)
+# --------------------------------------------------
+# AGENT
+# --------------------------------------------------
+prompt = hub.pull("hwchase17/openai-functions-agent")
+
+llm = ChatOpenAI(
+    model="gpt-3.5-turbo",
+    temperature=0,
+)
 
+agent = create_openai_functions_agent(
+    llm=llm,
+    tools=tools,
+    prompt=prompt,
+)
+
+agent_executor = AgentExecutor(
+    agent=agent,
+    tools=tools,
+    verbose=True,
+)
+
+# --------------------------------------------------
+# GRADIO APP
+# --------------------------------------------------
 def answer_question(question):
-    """Function to process user question and return agent response"""
     if not question.strip():
         return "Please enter a question."
-    
+
     try:
-        result = agent_executor.invoke({"input": question})
-        return result["output"]
+        response = agent_executor.invoke({"input": question})
+        return response["output"]
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error: {e}"
+
 
-# Create Gradio Interface
 demo = gr.Interface(
     fn=answer_question,
     inputs=gr.Textbox(
         label="Ask about GitHub Issues",
         placeholder="Type your question here...",
-        lines=3
-    ),
-    outputs=gr.Textbox(
-        label="Response",
-        lines=5
+        lines=3,
     ),
+    outputs=gr.Textbox(label="Response", lines=6),
     title="GitHub Issues AI Agent",
-    description="Ask questions about GitHub issues using AI-powered semantic search",
+    description="Ask questions about GitHub issues using AI-powered semantic search.",
     examples=[
         ["What are the recent issues?"],
-        ["Tell me about bug reports"],
-        ["What features are being requested?"]
+        ["Are there any open bugs?"],
+        ["What features are being requested?"],
     ],
-    allow_flagging="never"
 )
 
 if __name__ == "__main__":