import gradio as gr
from transformers import pipeline
import numpy as np


roberta_base_detector = pipeline("text-classification", model="Models/fine_tuned/roberta-base-openai-detector-model", tokenizer="Models/fine_tuned/roberta-base-openai-detector-tokenizer")
chatgpt_lli_hc3_detector = pipeline("text-classification", model="Models/fine_tuned/chatgpt-detector-lli-hc3-model", tokenizer="Models/fine_tuned/chatgpt-detector-lli-hc3-tokenizer")
chatgpt_roberta_detector = pipeline("text-classification", model="Models/fine_tuned/chatgpt-detector-roberta-model", tokenizer="Models/fine_tuned/chatgpt-detector-roberta-tokenizer")

def classify_text(text):
    # Get predictions from each model
    roberta_base_pred = 1 if roberta_base_detector(text)[0]['label'] == "Fake" else: 0
    chatgpt_lli_hc3_pred = chatgpt_lli_hc3_detector(text)[0]['label']
    chatgpt_roberta_pred = chatgpt_roberta_detector(text)[0]['label']

    # Count the votes for AI and Human
    votes = {"AI": 0, "Human": 0}
    for pred in [roberta_base_pred, chatgpt_lli_hc3_pred, chatgpt_roberta_pred]:
        if pred == 1:
            votes["AI"] += 1
        else:
            votes["Human"] += 1

    # Determine final decision based on majority
    if votes["AI"] > votes["Human"]:
        return chatgpt_lli_hc3_pred
    else:
        return chatgpt_lli_hc3_pred

# Create Gradio Interface
iface = gr.Interface(
    fn=classify_text, 
    inputs=gr.Textbox(lines=2, placeholder="Enter a sentence to classify..."),
    outputs="text"
)

iface.launch()