Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from huggingface_hub import snapshot_download | |
| # Set device to CPU | |
| device = "cpu" | |
| repo_id = 'amgadhasan/phi-2' | |
| model_path = snapshot_download(repo_id=repo_id, repo_type="model", local_dir="./phi-2", use_auth_token=False) | |
| tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | |
| # Set default dtype to float32 for compatibility with CPU | |
| torch.set_default_dtype(torch.float32) | |
| model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto", trust_remote_code=True) | |
| def generate(prompt): | |
| inputs = tokenizer(prompt, return_tensors="pt").to(device) | |
| outputs = model.generate(**inputs, max_length=200) | |
| completion = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return completion | |
| def ask_question(user_question): | |
| if user_question.lower() == 'quit': | |
| return "Session ended. Goodbye!" | |
| else: | |
| # Here, we're explicitly setting the context for an academic answer. | |
| prompt = f"Academic response to the question about basic science subjects: {user_question}" | |
| answer = generate(prompt) | |
| return answer | |
| iface = gr.Interface(fn=ask_question, inputs="text", outputs="text") | |
| iface.launch(share=True) |