Update app.py

app.py

@@ -1,17 +1,20 @@
 import gradio as gr
 import os
 import requests
+
 import uvicorn
 from huggingface_hub import InferenceClient
 from fastapi import FastAPI
 from pydantic import BaseModel
 
+
 # Initialize FastAPI
 app = FastAPI()
 
 # Hugging Face Model for Gradio & API
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+
 # Telegram Bot Token (Replace with your actual token)
 TELEGRAM_BOT_TOKEN = "7880465873:AAFDakza41P2drekyUzv-2o8Yp-TBRU7TiU"
 TELEGRAM_API_URL = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
@@ -25,10 +28,10 @@ class Message(BaseModel):
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message="You are a helpful Assistant. You let the user know if the information they share with you is true or false. You return the actual facts behind the information given, after stating the state of Truth or False.",
-    max_tokens=512,
-    temperature=0.7,
-    top_p=0.9,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
 ):
     messages = [{"role": "system", "content": system_message}]
 
@@ -50,15 +53,28 @@ def respond(
         top_p=top_p,
     ):
         token = message.choices[0].delta.content
+
         response += token
         yield response
 
-# ✅ Define Gradio Chatbot UI
+# Gradio UI
 demo = gr.ChatInterface(
-    respond, chatbot=gr.Chatbot(type="messages")
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a helpful Assistant. You let the user to know if the information they share with you is true or false. You return the actual facts behind the information given, after stating the state of Truth or False.", 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)",
+        ),
+    ],
 )
 
-# ✅ Mount Gradio UI to FastAPI
+# Mount Gradio UI on the root `/`
 app = gr.mount_gradio_app(app, demo, path="/")
 
 @app.post("/predict")
@@ -70,7 +86,26 @@ def predict(data: Message):
 
     # Send response back to Telegram user
     requests.post(TELEGRAM_API_URL, json={"chat_id": data.chat_id, "text": bot_reply})
+
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     return {"response": bot_reply}
 
+
+
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)