app.py

import os
import pandas as pd
from rank_bm25 import BM25Okapi
import gradio as gr
import openai
from datasets import load_dataset

# Load Hugging Face dataset
HF_TOKEN = os.getenv("HF_TOKEN")
if not HF_TOKEN:
    raise ValueError("Hugging Face token is not set. Please set HF_TOKEN as an environment variable.")

# Explicitly define dataset file paths
data_files = {
    "train": "hf://datasets/farah1/mental-health-posts-classification/train.csv",
    "validation": "hf://datasets/farah1/mental-health-posts-classification/validation.csv",
}

try:
    print("Loading dataset...")
    dataset = load_dataset("csv", data_files=data_files)
    train_data = dataset["train"].to_pandas()
    print("Dataset loaded successfully.")
except Exception as e:
    print(f"Failed to load dataset: {e}")
    train_data = pd.DataFrame()

# Check and create the 'text' column
if "text" not in train_data.columns:
    if "title" in train_data.columns and "content" in train_data.columns:
        train_data["text"] = train_data["title"] + " " + train_data["content"]
    else:
        raise ValueError("The 'text' column is missing, and the required 'title' and 'content' columns are not available to create it.")

# Initialize BM25
tokenized_train = [doc.split() for doc in train_data["text"]]
bm25 = BM25Okapi(tokenized_train)

# Few-shot classification function
def classify_text(api_key, input_text, k=20):
    # Set the API key
    openai.api_key = api_key
    if not openai.api_key:
        return "Error: OpenAI API key is not set."

    # Tokenize input text
    tokenized_text = input_text.split()
    # Get top-k similar examples using BM25
    scores = bm25.get_scores(tokenized_text)
    top_k_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]

    # Build examples for the prompt
    examples = "\n".join(
        f"Example {i+1}:\nText: {train_data.iloc[idx]['text']}\nClassification: "
        f"Stress={train_data.iloc[idx]['Ground_Truth_Stress']}, "
        f"Anxiety={train_data.iloc[idx]['Ground_Truth_Anxiety']}, "
        f"Depression={train_data.iloc[idx]['Ground_Truth_Depression']}, "
        f"Other={train_data.iloc[idx]['Ground_Truth_Other_binary']}\n"
        for i, idx in enumerate(top_k_indices)
    )

    # Construct OpenAI prompt
    prompt = f"""
    You are a mental health specialist. Analyze the provided text and classify it into one or more of the following categories: Stress, Anxiety, Depression, or Other.

    Respond with a single category that best matches the content: Stress, Anxiety, Depression, or Other.

    Here is the text to classify:
    "{input_text}"

    ### Examples:
    {examples}
    """

    try:
        response = openai.ChatCompletion.create(
            messages=[
                {"role": "system", "content": "You are a mental health specialist."},
                {"role": "user", "content": prompt},
            ],
            model="gpt-4",
            temperature=0,
        )
        content = response.choices[0].message.content.strip()
        print("OpenAI Response:", content)
        return content  # Return the label directly
    except Exception as e:
        print(f"Error occurred: {e}")
        return f"Error: {e}"

# Enhanced Gradio Interface with Examples
with gr.Blocks() as interface:
    gr.Markdown(
        """
        # 🧠 Mental Health Text Classifier
        Welcome to the Mental Health Text Classifier. Enter your OpenAI API key and input text, and the system will classify it into one of the following categories:
        **Stress**, **Anxiety**, **Depression**, or **Other**.

        > **Disclaimer**: This tool is an AI-based model and may not always provide accurate results. It is not a substitute for professional psychological advice. If you have concerns about your mental health, please consult a licensed psychologist or mental health professional.
        """
    )
    with gr.Row():
        api_key_input = gr.Textbox(
            label="🔑 OpenAI API Key",
            placeholder="Enter your OpenAI API key here...",
            type="password",
        )
    with gr.Row():
        example_texts = gr.Dropdown(
            label="🔎 Example Texts",
            choices=[
                "I feel like I'm constantly under pressure at work. The deadlines keep piling up, and I can't seem to catch a break. I'm exhausted and overwhelmed.",  # Stress
                "Lately, I’ve been feeling really anxious about social situations. I keep overthinking what people might think of me, and it’s making me avoid going out altogether.",  # Anxiety
                "I have no energy or motivation to do anything. Even getting out of bed feels like a struggle, and nothing seems to make me happy anymore.",  # Depression
            ],
            value="Select an example text or type your own below.",
            interactive=True,
        )
    with gr.Row():
        text_input = gr.Textbox(
            label="📝 Input Text",
            lines=5,
            placeholder="Enter your thoughts or feelings here...",
        )
    with gr.Row():
        submit_button = gr.Button("Classify")
    with gr.Row():
        output_text = gr.Textbox(
            label="🏷️ Classification Result",
            placeholder="The classification result will appear here.",
            interactive=False,
        )

    # Update the text input field when an example is selected
    example_texts.change(fn=lambda x: x, inputs=example_texts, outputs=text_input)

    submit_button.click(
        classify_text,
        inputs=[api_key_input, text_input],
        outputs=output_text,
    )

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