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| import torch | |
| import gradio | |
| from transformers import AutoModelWithLMHead, AutoTokenizer | |
| def generate_response(model, tokenizer, prompt, max_length=200): | |
| input_ids = tokenizer.encode(prompt, return_tensors="pt") # 'pt' for returning pytorch tensor | |
| # Create the attention mask and pad token id | |
| attention_mask = torch.ones_like(input_ids) | |
| pad_token_id = tokenizer.eos_token_id | |
| output = model.generate( | |
| input_ids, | |
| max_length=max_length, | |
| num_return_sequences=1, | |
| attention_mask=attention_mask, | |
| pad_token_id=pad_token_id | |
| ) | |
| return tokenizer.decode(output[0], skip_special_tokens=True) | |
| # Load your model from hub | |
| username = "vsen7" # change it to your HuggingFace username | |
| checkpoint = username + '/Medical_Summary' | |
| loaded_model = AutoModelWithLMHead.from_pretrained(checkpoint) | |
| # Load your tokenizer from hub | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| # Function for response generation | |
| def generate_query_response(prompt): | |
| model = loaded_model | |
| #tokenizer = tokenizer | |
| response = generate_response(model, tokenizer, prompt) | |
| return response | |
| # Gradio interface to generate UI link | |
| iface = gradio.Interface(fn=generate_query_response, | |
| inputs="textbox", | |
| outputs="textbox", | |
| title="Medical Summary", | |
| description="via gradio", | |
| allow_flagging="never", | |
| ) | |
| iface.launch() |