Update app.py

app.py

@@ -1,32 +1,25 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import os
-from huggingface_hub import login
 import gradio as gr
 
-# 1. Authenticate with Hugging Face token from secrets
+# Get Hugging Face token from environment variable (safe in secrets)
 hf_token = os.environ.get("hf_space_token")
-login(token=hf_token)
 
-# 2. Load Gemma model and tokenizer (GATED model needs token)
 model_name = "google/gemma-3-1b-it"
 tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
 model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)
 
-# 3. Define response generation function
 def generate_response(prompt):
     inputs = tokenizer(prompt, return_tensors="pt")
     outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# 4. Create Gradio interface
 iface = gr.Interface(
     fn=generate_response,
     inputs=gr.Textbox(lines=2, placeholder="Ask something..."),
     outputs="text",
     title="Chat with Gemma",
-    description="This chatbot is powered by Google's Gemma model running in Hugging Face Spaces."
+    description="Chatbot powered by Google's Gemma model"
 )
 
-# 5. Launch app
 iface.launch()
-