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b4f6c62 897ff18 b4f6c62 897ff18 b4f6c62 897ff18 b4f6c62 897ff18 b4f6c62 f1a00d0 897ff18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
MODEL = "docto/Docto-Bot"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
def get_reply(user_input):
prompt = f"Question: {user_input}\nAnswer:"
inputs = tokenizer(
prompt,
return_tensors="pt"
).to(device)
outputs = model.generate(
**inputs,
max_new_tokens=150,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.9,
repetition_penalty=1.15,
no_repeat_ngram_size=3,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id
)
response = tokenizer.decode(
outputs[0],
skip_special_tokens=True
)
if "Answer:" in response:
response = response.split("Answer:", 1)[1]
return response.strip()
iface = gr.Interface(
fn=get_reply,
inputs=gr.Textbox(
lines=2,
placeholder="Ask a medical question..."
),
outputs=gr.Textbox(
label="Response"
),
title="Docto-Bot",
description="Medical Question Answering Bot"
)
iface.launch() |