Update app.py

app.py

@@ -1,86 +1,15 @@
-import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+from ai_assistant import get_response
 
+def respond(message, history):
+    return get_response(message, history)
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken | None,
-):
-    """
-    Chat handler for Hugging Face Inference API.
-    Works with both HF Spaces (OAuth) and local execution (HF_TOKEN env var).
-    """
-
-    # ✅ Get token from Spaces OAuth or local env
-    token = None
-    if hf_token and getattr(hf_token, "token", None):
-        token = hf_token.token
-    else:
-        token = os.getenv("HF_TOKEN")
-
-    if not token:
-        yield "❌ No Hugging Face token found. Please log in or set HF_TOKEN."
-        return
-
-    # ✅ Choose a model that supports Inference API
-    model_id = "mistralai/Mistral-7B-Instruct-v0.2"  # change if needed
-    client = InferenceClient(token=token, model=model_id)
-
-    # ✅ Build messages list
-    messages = [{"role": "system", "content": system_message}]
-    messages.extend(history)
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    # ✅ Streaming response
-    for message_chunk in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message_chunk.choices
-        token = ""
-        if len(choices) and choices[0].delta and choices[0].delta.content:
-            token = choices[0].delta.content
-
-        response += token
-        yield response
-
-
-# ✅ Gradio UI
 chatbot = gr.ChatInterface(
     respond,
     type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a helpful research assistant.", 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)",
-        ),
-    ],
+    title="Research Query Assistant",
+    description="Ask research questions, get answers from Mistral on Hugging Face."
 )
 
-with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-        gr.Markdown("🔑 Log in with your Hugging Face account to run the model.")
-    chatbot.render()
-
-
 if __name__ == "__main__":
-    # ✅ share=True → generates public link when running locally
-    demo.launch(share=True)
+    chatbot.launch()