feat: initial commit

.env.example

@@ -0,0 +1,3 @@
+AZURE_OPENAI_ENDPOINT=
+AZURE_OPENAI_API_KEY=
+AZURE_OPENAI_VECTOR_STORE_ID=

.gitignore

@@ -0,0 +1,4 @@
+.env*
+!.env.example
+.venv
+__pycache__

app.py

@@ -1,64 +1,147 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+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="指示がない限り、日本語で回答してください。",
+        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_annotations(msg):
+    annotations = msg.content[0].text.annotations
+    file_ids = []
+    if annotations:
+        for annotation in annotations:
+            file_id = annotation.file_citation.file_id
+            if file_id in file_ids:
+                continue
+
+            print("file_id", file_id)
+
+            cited_file = client.files.retrieve(file_id)
+
+            print("filename", cited_file.filename)
+
+            try:
+                content = client.files.content(file_id)
+            except Exception as e:
+                print(e)
+                pass
+
+            file_ids.append(file_id)
+
+    return file_ids
+
+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:
+            # file_ids = get_annotations(msg)
+
+            main_text = msg.content[0].text.value
+            # main_text += "\n> aaa"
+            return main_text
+
+    elif run.status == "requires_action":
+        # Handle cases where the assistant requires further action
+        pass
+
+    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:
+    chatbot = gr.Chatbot(type="messages")
+    msg = gr.Textbox()
+    clear = gr.ClearButton([msg, chatbot])
+
+    def respond(message, chat_history):
+
+        bot_message = get_chatbot_response(client, thread.id, assistant.id, message)
+
+        chat_history.append({"role": "user", "content": message})
+        chat_history.append({"role": "assistant", "content": bot_message})
+        return "", chat_history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+
+    clear.click(clear_thread, [chatbot])
 
 if __name__ == "__main__":
     demo.launch()

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.25.2
+gradio
+openai
+python-dotenv