Update app.py

app.py

@@ -1,39 +1,20 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 import gradio as gr
 
-# Load model and tokenizer from Hugging Face
-model_name = "Salesforce/codegen-350M-multi"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Load a pre-hosted code-generation model Space (no token needed)
+generator = gr.Interface.load("huggingface/HuggingFaceH4/starchat-alpha")
 
-# Set device (GPU if available)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
+def generate_code(prompt, max_tokens):
+    return generator(prompt, max_tokens)[0]
 
-def generate_code(prompt, max_length=100):
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    outputs = model.generate(
-        **inputs,
-        max_length=len(inputs["input_ids"][0]) + max_length,
-        temperature=0.7,
-        do_sample=True,
-        top_p=0.95,
-        pad_token_id=tokenizer.eos_token_id
-    )
-    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return generated_code
-
-# Gradio Interface
 interface = gr.Interface(
     fn=generate_code,
     inputs=[
-        gr.Textbox(lines=5, label="Code Prompt"),
+        gr.Textbox(label="Code Prompt", lines=5, placeholder="e.g., def add(a, b):"),
         gr.Slider(20, 300, step=10, label="Max Tokens", value=100),
     ],
     outputs=gr.Textbox(label="Generated Code"),
-    title="🧠 Code Generator using HuggingFace",
-    description="Enter a prompt like `def factorial(n):` and let the AI complete the code."
+    title="🧠 Code Generator (No Token Required)",
+    description="Powered by Hugging Face hosted model. No API key needed."
 )
 
 if __name__ == "__main__":