app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login  # Import login function
import torch
import os
import re

login(token=os.getenv("HF_TOKEN"))

model_path = "KomalNaseem/depression-chatbot-model"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path).to(device)
model.eval()

user_history = []
turn_counter = 0
MAX_TURNS_FOR_PREDICTION = 8

def chat(user_input):
    global user_history, turn_counter
    turn_counter += 1

    user_history.append(f"Human: {user_input}")

    last_turns = user_history[-4:]
    prompt = "\n".join(last_turns) + "\nAI:"

    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    output_ids = model.generate(
        **inputs,
        max_new_tokens=50,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )
    response_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    response = response_text.split("AI:")[-1].strip()

    user_history.append(f"AI: {response}")

    depression_prob = None
    if turn_counter == MAX_TURNS_FOR_PREDICTION:
        prediction_prompt = (
            "\n".join(user_history[-8:]) +
            "\nAI: Based on this conversation, what is the probability that the human has depression? "
            "Please answer with a number between 0 and 1."
        )
        inputs_pred = tokenizer(prediction_prompt, return_tensors="pt").to(device)
        output_pred_ids = model.generate(
            **inputs_pred,
            max_new_tokens=10,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
        pred_text = tokenizer.decode(output_pred_ids[0], skip_special_tokens=True)

        match = re.search(r"0?\.\d+", pred_text)
        if match:
            try:
                depression_prob = float(match.group(0))
            except:
                depression_prob = None

    return response, depression_prob if depression_prob is not None else "Prediction after 8 turns"

iface = gr.Interface(
    fn=chat,
    inputs=gr.Textbox(lines=2, label="Your Message"),
    outputs=[gr.Textbox(label="AI Response"), gr.Textbox(label="Depression Probability")],
    title="Depression Detection Chatbot",
    description="Chat with the AI. After 8 turns it predicts depression probability."
)

if __name__ == "__main__":
    iface.launch()