Create app.py

app.py

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+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)
+
+# Set device (GPU if available)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+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.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."
+)
+
+if __name__ == "__main__":
+    interface.launch()