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import gradio as gr
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Tianlin668/MentalBART")
model = AutoModelForSeq2SeqLM.from_pretrained("Tianlin668/MentalBART")
# Define the inference function
def inference(prompt):
# Encode the input prompt
input_ids = tokenizer.encode(prompt, return_tensors="pt")
# Generate a response
# You can adjust max_length and num_beams according to your needs
output = model.generate(input_ids, max_length=150, num_beams=5, early_stopping=True)
# Decode the generated output
result = tokenizer.decode(output[0], skip_special_tokens=True)
# Return the generated response from the model
return result
with gr.Blocks() as demo:
gr.Markdown("<center><h1> This is your virtual assistant - MentalBART model </h1></center>")
gr.Markdown("<center><h2> This assistant will try to diagnose for depression and provide reasoning for it. </h2></center>")
# Input boxes for the prompt and Hugging Face token
prompt = gr.Textbox(label="Prompt", lines=3, max_lines=5)
# Button for generating response
generate_btn = gr.Button("Generate Response")
output = gr.Markdown("Response will appear here.")
# Trigger the inference function on button click
generate_btn.click(fn=inference, inputs=[prompt], outputs=[output])
if __name__ == "__main__":
demo.launch()
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