Update app.py

app.py

@@ -1,13 +1,36 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("Lucid-research/lucentcode-1-py")
-model = AutoModelForCausalLM.from_pretrained("Lucid-research/lucentcode-1-py")
+MODEL_ID = "Lucid-research/lucentcode-1-py"  # Change this to your model repo ID
 
-def generate_text(prompt):
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
+
+def format_prompt(user_input):
+    return f"### Instruction:\n{user_input}\n\n### Output:\n"
+
+def generate_code(user_input):
+    prompt = format_prompt(user_input)
     inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_length=1000)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+    outputs = model.generate(
+        **inputs,
+        max_length=1000,
+        temperature=0.7,
+        do_sample=True,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Return only the generated part after "### Output:"
+    return text.split("### Output:")[-1].strip()
+
+iface = gr.Interface(
+    fn=generate_code,
+    inputs=gr.Textbox(lines=4, label="Instruction"),
+    outputs=gr.Textbox(lines=8, label="Generated Output"),
+    title="Code Generation with Your Model",
+    description="Enter an instruction and get a generated Python function.",
+)
 
-iface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
-iface.launch()
+if __name__ == "__main__":
+    iface.launch()