app.py

import gradio as gr
from transformers import pipeline, BertTokenizer, AutoModelForSequenceClassification
import torch
import os
from groq import Groq

# Set the GROQ_API_KEY environment variable (this is your API key for Groq)
api_key = os.getenv("GROQ_API_KEY")
if not api_key:
    raise ValueError("GROQ_API_KEY is missing. Please add it as a secret in Hugging Face Spaces.")

# Initialize the Groq client
client = Groq(api_key=api_key)

# Load the saved models and tokenizers for bullying detection
bullying_model_path = 'kairaamilanii/IndoBERT-Bullying-Classifier'
bullying_tokenizer = BertTokenizer.from_pretrained(bullying_model_path)
bullying_model = AutoModelForSequenceClassification.from_pretrained(bullying_model_path)

# Load the emotion detection pipeline
emotion_model_path = "StevenLimcorn/indonesian-roberta-base-emotion-classifier"
emotion_pipeline = pipeline(
    "sentiment-analysis",
    model=emotion_model_path,
    tokenizer=emotion_model_path
)

# Function to query Groq API for suggestions or feedback on the comment in Indonesian
def get_llm_suggestions(comment, bullying_result, emotion_label):
    # Use the Groq API to get feedback or suggestions
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": (
                    f"Beri saran atau umpan balik untuk meningkatkan komentar berikut dalam bahasa Indonesia: "
                    f"'{comment}' jika terdeteksi '{bullying_result}' dengan emosi yang dideteksi sebagai "
                    f"'{emotion_label}'. Berikan peringatan jika komentar terlalu kasar."
                )
            }
        ],
        model="gemma2-9b-it"  # You can replace this with any other model you'd like to use
    )

    response = chat_completion.choices[0].message.content
    return response

# Define the function for Gradio interface
def detect_bullying_and_emotion(comment):
    # Tokenize the input text for bullying detection
    bullying_inputs = bullying_tokenizer(comment, return_tensors="pt")

    # Make the bullying detection prediction
    with torch.no_grad():
        bullying_outputs = bullying_model(**bullying_inputs)
        bullying_predicted_class = torch.argmax(bullying_outputs.logits, dim=-1).item()

    # Make the emotion detection prediction using the pipeline
    emotion_result = emotion_pipeline(comment)

    # Interpreting the bullying result
    if bullying_predicted_class == 1:
        bullying_result = "Bullying"
        feedback_message = "Apakah Anda yakin ingin mengirim komentar ini? Komentar ini terdeteksi sebagai bullying."
    else:
        bullying_result = "Non-bullying"
        feedback_message = "Komentar ini aman untuk dikirim."

    # Emotion result (label and confidence)
    emotion_label = emotion_result[0]['label']
    emotion_score = emotion_result[0]['score']

    # Get LLM suggestions for improving the comment
    llm_suggestions = get_llm_suggestions(comment, bullying_result, emotion_label)

    # Return the results along with feedback message and suggestions
    return bullying_result, f"{emotion_label} (Confidence Score: {emotion_score:.2f})", feedback_message, llm_suggestions

# Create Gradio interface
iface = gr.Interface(
    fn=detect_bullying_and_emotion,        # The function that processes the input
    inputs=gr.Textbox(label="Masukkan komentar Anda", lines=4),  # Text input for the comment
    outputs=[
    gr.Textbox(label="Deteksi Bullying"),  # Output 1: Bullying result
    gr.Textbox(label="Deteksi Emosi"),     # Output 2: Emotion detection
    gr.Textbox(label="Feedback"), # Output 3: Feedback message
    gr.Textbox(label="Saran Perbaikan AI")],  # Output 4: LLM Suggestions]
    live=False,                            # Don't analyze live, only after submit
    title="Sistem Evaluasi Komentar",     # Title of the interface
    description="Masukkan komentar untuk memeriksa bullying, deteksi emosi, dan saran perbaikan."  # Description
)

# Launch the interface
iface.launch()