app.py

import gradio as gr
import torch
from transformers import GPT2Tokenizer, AutoModelForCausalLM
from peft import PeftModel

# 1️⃣ Load fallback tokenizer (GPT2)
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
tokenizer.pad_token = tokenizer.eos_token  # Required for causal LM

# 2️⃣ Load base model
base_model_name = "TRM-coding/PythonCopilot"
device = "cuda" if torch.cuda.is_available() else "cpu"

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device)

# 3️⃣ Resize embeddings to match PEFT checkpoint vocab
checkpoint_vocab_size = 50257  # From DSUDUDe/funfox PEFT model
base_model.resize_token_embeddings(checkpoint_vocab_size)

# 4️⃣ Load PEFT/LoRA adapter
peft_model_name = "DSDUDEd/funfox"
model = PeftModel.from_pretrained(base_model, peft_model_name)
model.eval()

# 5️⃣ Define generation function
def generate_text(prompt, max_tokens=50):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        do_sample=True,
        top_p=0.9,
        temperature=0.8
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# 6️⃣ Build Gradio interface
iface = gr.Interface(
    fn=generate_text,
    inputs=[
        gr.Textbox(label="Enter Prompt", lines=2, placeholder="Type something..."),
        gr.Slider(minimum=10, maximum=200, step=10, label="Max Tokens")
    ],
    outputs=gr.Textbox(label="Generated Text"),
    title="FunFox PEFT Model",
    description="FunFox LoRA model fine-tuned on PythonCopilot base."
)

# 7️⃣ Launch
iface.launch(share=True)