Create app.py

app.py

@@ -0,0 +1,101 @@
+from dotenv import load_dotenv
+import os
+
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_astradb import AstraDBVectorStore
+from langchain.agents import create_tool_calling_agent
+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 import hub
+from note import note_tool
+
+load_dotenv()
+
+def connet_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(
+        embedding=embeddings,
+        collection_name="github",
+        namespace=ASTRA_DB_KEYSPACE,
+        api_endpoint=ASTRA_DB_API_ENDPOINT,
+        token=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:
+    owner = "Ini-design"
+    repo = "register"
+    issues = fetch_github_issues(owner, repo)
+    try:
+        vstore.delete_collection()
+    except:
+        pass
+        
+    vstore = connet_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 = create_retriever_tool(
+    retriever,
+    "github_search",
+    "search for information aabout github issues. For any question abour github issue, you must used this tools!"
+)
+
+prompt = hub.pull("hwchase17/openai-functions_agent")
+llm = ChatOpenAI()
+
+tools = [retriever_tool], note_tool
+agent = create_tool_calling_agent(llm, tools, prompt)
+agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+
+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"]
+    except Exception as e:
+        return f"Error: {str(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
+    ),
+    title="GitHub Issues AI Agent",
+    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?"]
+    ],
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch(debug=False)