Spaces:
Running
Running
File size: 1,601 Bytes
2098428 22209ca 2098428 22209ca 2098428 2d0ce20 2098428 2d0ce20 2098428 |
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 |
import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Set up the device to use CPU only
device = torch.device("cpu")
# Load model and tokenizer, then move the model to the appropriate device
model = AutoModelForCausalLM.from_pretrained("adi2606/MenstrualQA").to(device)
tokenizer = AutoTokenizer.from_pretrained("adi2606/MenstrualQA")
# Function to generate a response from the chatbot
def generate_response(message: str, temperature: float = 0.4, repetition_penalty: float = 1.1, max_input_length: int = 256) -> str:
inputs = tokenizer(
message,
return_tensors="pt",
padding=True,
truncation=True,
max_length=max_input_length
).to(device)
# Generate the response
output = model.generate(
inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=512,
temperature=temperature,
repetition_penalty=repetition_penalty,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)
# Decode the generated output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
return generated_text
# Streamlit app layout
st.title("Menstrual QA Chatbot")
st.write("Ask any question related to menstrual health.")
# User input
user_input = st.text_input("You:", "")
if st.button("Send"):
if user_input:
with st.spinner("Generating response..."):
response = generate_response(user_input)
st.write(f"Chatbot: {response}")
else:
st.write("Please enter a question.")
|