fix: bug

app.py

@@ -127,6 +127,107 @@ assistant = create_assistant(client, vector_store_id)
 thread = create_thread(client)
 
 with gr.Blocks() as demo:
+    import gradio as gr
+from openai import AzureOpenAI
+import os
+from dotenv import load_dotenv
+import time
+
+def load_environment():
+    """Load environment variables."""
+    load_dotenv(override=True)
+
+def initialize_openai_client():
+    """Initialize the Azure OpenAI client."""
+    return AzureOpenAI(
+        azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+        api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+        api_version="2024-10-01-preview"
+    )
+
+def create_assistant(client, vector_store_id):
+    """Create an assistant with specified configuration."""
+    return client.beta.assistants.create(
+        model="gpt-4o",
+        instructions="",
+        tools=[{
+            "type": "file_search",
+            "file_search": {"ranking_options": {"ranker": "default_2024_08_21", "score_threshold": 0}}
+        }],
+        tool_resources={"file_search": {"vector_store_ids": [vector_store_id]}},
+        temperature=0
+    )
+
+def create_thread(client):
+    """Create a new thread."""
+    return client.beta.threads.create()
+
+def clear_thread(_):
+    """Clear the chat history and reset the thread."""
+    global thread
+    thread = create_thread(client)
+    return [], ""
+
+def get_chatbot_response(client, thread_id, assistant_id, message):
+    """Get chatbot response for a given message."""
+    client.beta.threads.messages.create(
+        thread_id=thread_id,
+        role="user",
+        content=message  # Ensure the content is an object with a `text` key
+    )
+
+    run = client.beta.threads.runs.create(
+        thread_id=thread_id,
+        assistant_id=assistant_id
+    )
+
+    while run.status in ["queued", "in_progress", "cancelling"]:
+        time.sleep(1)
+        run = client.beta.threads.runs.retrieve(
+            thread_id=thread_id,
+            run_id=run.id
+        )
+
+    if run.status == "completed":
+        messages = client.beta.threads.messages.list(thread_id=thread_id)
+
+        for msg in messages:
+            main_text = msg.content[0].text.value
+            return main_text
+
+    return "Unable to retrieve a response."  # Fallback response
+
+def chatbot_response(history, message):
+    """Wrapper function to generate chatbot response."""
+    global thread
+    # Get response from the API
+    assistant_response = get_chatbot_response(client, thread.id, assistant.id, message)
+
+    # Update chat history
+    history.append({"role": "user", "content": message})
+    history.append({"role": "assistant", "content": assistant_response})
+
+    return history, ""
+
+# Load environment variables
+load_environment()
+
+# Initialize OpenAI client
+client = initialize_openai_client()
+
+# Define vector store ID
+vector_store_id = os.getenv("AZURE_OPENAI_VECTOR_STORE_ID")
+
+# Create assistant and thread
+assistant = create_assistant(client, vector_store_id)
+thread = create_thread(client)
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+        # Azure OpenAI Assistants API x Gradio x Zenn
+        This is a Gradio demo of Retrieval-Augmented Generation (RAG) using the Azure OpenAI Assistants API, applied to [Zenn articles](https://zenn.dev/nakamura196).
+    """)
+
     chatbot = gr.Chatbot(type="messages")
     msg = gr.Textbox()
     clear = gr.ClearButton([msg, chatbot])