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import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
import torch
# Load model and tokenizer
model_name = "ombhojane/mental-health-assistant"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
def generate_response(message, history):
# Format the input with chat history
prompt = ""
for user_msg, bot_msg in history:
prompt += f"User: {user_msg}\nAssistant: {bot_msg}\n"
prompt += f"User: {message}\nAssistant:"
# Generate response
response = pipe(
prompt,
max_length=256,
num_return_sequences=1,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.eos_token_id
)[0]["generated_text"]
# Extract just the assistant's response
try:
assistant_response = response.split("Assistant:")[-1].strip()
except:
assistant_response = response
return assistant_response
# Create the Gradio interface
with gr.Blocks(css="footer {visibility: hidden}") as demo:
gr.Markdown(
"""
# 🧠 Mental Health Assistant
Welcome! I'm here to provide support and guidance for your mental health concerns.
While I can offer helpful insights, please remember I'm not a replacement for professional medical advice.
"""
)
chatbot = gr.Chatbot(
height=400,
show_label=False,
container=True,
bubble_full_width=False,
)
with gr.Row():
msg = gr.Textbox(
scale=4,
show_label=False,
placeholder="Type your message here...",
container=False
)
submit = gr.Button("Send", scale=1, variant="primary")
gr.Examples(
examples=[
"I've been feeling really anxious lately",
"How can I improve my sleep habits?",
"I'm having trouble focusing at work",
],
inputs=msg
)
gr.Markdown(
"""
### Tips for best results:
- Be specific about how you're feeling
- Ask direct questions
- Share relevant context
- Take your time to explain your situation
"""
)
# Set up event handlers
submit_click = submit.click(
generate_response,
inputs=[msg, chatbot],
outputs=[chatbot],
queue=True
)
submit_click.then(lambda: "", None, msg, queue=False)
msg.submit(
generate_response,
inputs=[msg, chatbot],
outputs=[chatbot],
queue=True
).then(lambda: "", None, msg, queue=False)
demo.launch() |