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| import gradio as gr | |
| import torch | |
| import spaces | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # -------------------------------------------------- | |
| # Model configuration | |
| # -------------------------------------------------- | |
| REPO = "vermarjun/chad" | |
| SUBFOLDER = "transformers/dpo450" | |
| print("Loading Chad tokenizer...") | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| REPO, | |
| subfolder=SUBFOLDER, | |
| ) | |
| print("Loading Chad model...") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| REPO, | |
| subfolder=SUBFOLDER, | |
| torch_dtype=torch.float32, | |
| ) | |
| model.eval() | |
| print("Chad model loaded successfully!") | |
| # -------------------------------------------------- | |
| # Chat response function | |
| # -------------------------------------------------- | |
| def respond(message, history): | |
| if not message or not message.strip(): | |
| return "Bhai, kuch message toh likho!" | |
| # Build conversation prompt | |
| prompt = "" | |
| for item in history or []: | |
| if not isinstance(item, dict): | |
| continue | |
| role = item.get("role") | |
| content = item.get("content", "") | |
| # Ignore non-text messages | |
| if not isinstance(content, str): | |
| continue | |
| if role == "user": | |
| prompt += f"<|user|>{content}<|assistant|>" | |
| elif role == "assistant": | |
| prompt += f"{content}<|endoftext|>" | |
| prompt += f"<|user|>{message.strip()}<|assistant|>" | |
| # Tokenize input | |
| inputs = tokenizer( | |
| prompt, | |
| return_tensors="pt", | |
| add_special_tokens=False, | |
| ) | |
| # Generate response on the GPU allocated by ZeroGPU | |
| with torch.no_grad(): | |
| output = model.generate( | |
| **inputs, | |
| max_new_tokens=120, | |
| do_sample=True, | |
| temperature=0.8, | |
| top_p=0.9, | |
| repetition_penalty=1.3, | |
| eos_token_id=0, | |
| ) | |
| # Decode only newly generated tokens | |
| input_length = inputs["input_ids"].shape[1] | |
| new_tokens = output[0][input_length:] | |
| answer = tokenizer.decode( | |
| new_tokens, | |
| skip_special_tokens=True, | |
| ).strip() | |
| return answer or "Bhai, ek baar phir try karo." | |
| # -------------------------------------------------- | |
| # Gradio interface | |
| # -------------------------------------------------- | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| title="⚡ Chad AI", | |
| description="A Hinglish conversational AI chatbot", | |
| textbox=gr.Textbox( | |
| placeholder="Bol bhai, kya scene hai?", | |
| container=False, | |
| ), | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |