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# -*- coding: utf-8 -*-
"""app.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/11FAEDRYHuCI7iX5w3JaeKoD76-9pwrLi
"""
import os
import json
from rank_bm25 import BM25Okapi
import pandas as pd
import gradio as gr
import openai
# Load dataset
dataset_url = "https://huggingface.co/datasets/username/mental-health-classification/resolve/main/train.csv"
train_data = pd.read_csv(dataset_url)
train_data["text"] = train_data["title"] + " " + train_data["content"]
# Initialize BM25
tokenized_train = [doc.split() for doc in train_data["text"]]
bm25 = BM25Okapi(tokenized_train)
# Ensure the user sets their API key
if "OPENAI_API_KEY" not in os.environ:
raise ValueError("Please set your OpenAI API key using `os.environ['OPENAI_API_KEY'] = 'your_api_key'`")
# Initialize OpenAI API
openai.api_key = os.getenv("OPENAI_API_KEY")
# Few-shot classification function
def classify_text(input_text, k=20):
# 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 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. Classify the text into Stress, Anxiety, Depression, or Other:
### Examples:
{examples}
### Text to Classify:
"{input_text}"
### Output Format:
- **Ground_Truth_Stress**: 1 or 0
- **Ground_Truth_Anxiety**: 1 or 0
- **Ground_Truth_Depression**: 1 or 0
- **Ground_Truth_Other_binary**: 1 or 0
"""
try:
response = openai.ChatCompletion.create(
messages=[
{"role": "system", "content": "You are a mental health specialist."},
{"role": "user", "content": prompt},
],
model="gpt-4o",
temperature=0,
)
results = response.choices[0].message.content
return json.loads(results)
except Exception as e:
return str(e)
# Gradio Interface
interface = gr.Interface(
fn=classify_text,
inputs=gr.Textbox(lines=5, placeholder="Enter text for classification..."),
outputs="json",
title="Mental Health Text Classifier",
description="Classify text into Stress, Anxiety, Depression, or Other using BM25 and GPT-4.",
)
if __name__ == "__main__":
interface.launch() |