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app.py

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+
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load your fine-tuned model
+model_name = "Pradnya27/codegpt-finetuned-code-generation"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+tokenizer.pad_token = tokenizer.eos_token
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_code(question):
+    prompt = "Generate code: " + question
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs["input_ids"],
+            max_new_tokens=200,
+            temperature=0.7,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    
+    generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Return only the generated part after the prompt
+    return generated[len(prompt):]
+
+# Build the UI
+demo = gr.Interface(
+    fn=generate_code,
+    inputs=gr.Textbox(
+        label="Your coding question",
+        placeholder="e.g. Write a function to check if a number is prime",
+        lines=3
+    ),
+    outputs=gr.Code(
+        label="Generated Code",
+        language="python"
+    ),
+    title="🤖 CodeGPT — AI Code Generator",
+    description="Fine-tuned CodeGPT model by Pradnya27. Ask any coding question and get Python code!",
+    examples=[
+        ["Write a function to reverse a string"],
+        ["Write a function to find the largest number in a list"],
+        ["Write a function to check if a string is a palindrome"]
+    ]
+)
+
+demo.launch()