app.py

import torch
import gradio as gr
from transformers import pipeline, BertTokenizer, BertForQuestionAnswering
from datasets import load_dataset

# Load the dataset
advice_dataset = load_dataset("ziq/depression_advice")

# Load the fine-tuned BERT model and tokenizer
model_dir = "./bert-finetuned-depression"
model = BertForQuestionAnswering.from_pretrained(model_dir)
tokenizer = BertTokenizer.from_pretrained(model_dir)

# Extract context and messages
contexts = advice_dataset["train"]["text"]

# Define a function to generate answers
def generate_answer(messages):
    # If messages is a list, use the first message
    if isinstance(messages, list):
        messages = messages[0]
    
    # Tokenize the input message
    inputs = tokenizer(messages, return_tensors="pt")

    # Use the fine-tuned BERT model to generate the answer for the single message
    with torch.no_grad():
        outputs = model(**inputs)

    # Decode the output and return the answer
    answer_start = torch.argmax(outputs.start_logits)
    answer_end = torch.argmax(outputs.end_logits) + 1
    answer = tokenizer.decode(inputs["input_ids"][0][answer_start:answer_end])

    return answer if answer else "No answer found."

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_answer,
    inputs=[
        gr.Textbox(type="text", label="Message"),
    ],
    outputs=gr.Textbox(type="text", label="Answer"),
    title="Depression Advice Generator",
    description="Enter your feelings, and get supportive advice generated by a fine-tuned BERT model.",
)

# Launch the interface
iface.launch()