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import streamlit as st
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
import os
# Retrieve the Hugging Face token from environment variable
HUGGINGFACE_TOKEN = os.getenv("HF_ACCESS_TOKEN")
# Check if the token is available
if not HUGGINGFACE_TOKEN:
st.error("Hugging Face token not found. Please set the HUGGINGFACE_TOKEN environment variable.")
st.stop() # Stop the app if the token is not found
# Function to load model and tokenizer (local or Hugging Face with token)
def load_model(model_path):
try:
# Load tokenizer and model with authentication token if required
tokenizer = AutoTokenizer.from_pretrained(model_path, use_auth_token=HUGGINGFACE_TOKEN)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path, use_auth_token=HUGGINGFACE_TOKEN)
return tokenizer, model
except Exception as e:
st.error(f"Error loading model: {e}")
return None, None
# Set device (use GPU if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Path to your model (either a local path or a Hugging Face model name)
model_path = "Izza-shahzad-13/fine-tuned-flan-t5" # Use your Hugging Face model identifier
# Load tokenizer and model
tokenizer, model = load_model(model_path)
if model and tokenizer:
model.to(device)
else:
st.stop() # Stop the app if model or tokenizer failed to load
# Function to generate response from the model
def generate_response(input_text):
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512).to(device)
with torch.no_grad():
output = model.generate(
inputs['input_ids'],
max_length=500,
num_beams=4,
top_p=0.9,
top_k=50,
temperature=0.7,
do_sample=True,
no_repeat_ngram_size=3,
early_stopping=True
)
response = tokenizer.decode(output[0], skip_special_tokens=True)
return response
# Streamlit app interface
st.title("FLAN-T5 Mental Health Counseling Assistant")
st.write("Type your thoughts or feelings, and let the model respond.")
# User input for interaction
user_input = st.text_area("How are you feeling today?", placeholder="Type here...")
# Generate and display model response when input is provided
if user_input.strip(): # Check if input is not empty
with st.spinner("Generating response..."):
response = generate_response(user_input)
st.write("Model Response:", response)
else:
st.info("Please enter your thoughts or feelings in the text area above.") |