Update app.py

app.py

@@ -2,17 +2,14 @@ from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse
 from transformers import AutoModelForCausalLM, AutoTokenizer
 import torch
-import gradio as gr
-import requests
-import threading
 
 app = FastAPI()
 
-# Load model and tokenizer once
+# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
 model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
 
-# In-memory chat history by user
+# In-memory history per user
 chat_history = {}
 
 @app.get("/")
@@ -38,31 +35,7 @@ async def chat(request: Request):
     chat_history[user_id] = [bot_input_ids, output_ids]
     return JSONResponse({"reply": response})
 
-# Gradio UI to call your /ai endpoint easily via browser
-def gradio_chat(user_input, user_id="default"):
-    if not user_input:
-        return "Please enter some text."
-    url = f"https://Trigger82--API.hf.space/ai?query={user_input}&user_id={user_id}"
-    try:
-        res = requests.get(url)
-        if res.status_code == 200:
-            return res.json().get("reply", "No reply")
-        return f"Error: {res.status_code}"
-    except Exception as e:
-        return f"Exception: {e}"
-
-iface = gr.Interface(
-    fn=gradio_chat,
-    inputs=[gr.Textbox(label="Your Message"), gr.Textbox(label="User ID", value="default")],
-    outputs="text",
-    title="Chat with DialoGPT API",
-    description="Type your message and user id to chat with the model."
-)
-
-# Launch Gradio app in a thread alongside FastAPI
-def run_gradio():
-    iface.launch(server_name="0.0.0.0", server_port=7861, share=False)
-
-threading.Thread(target=run_gradio).start()
-
-# No need for uvicorn.run here on Spaces; it manages startup automatically
+# Only needed if running locally, not in Hugging Face Space
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)