app.py

import gradio as gr
from huggingface_hub import InferenceClient
import os

# 🔑 Load Hugging Face API Token
HF_API_TOKEN = os.getenv("HF_API_TOKEN")

# Initialize Hugging Face model client with authentication
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta", token=HF_API_TOKEN)

# System message setting the chatbot's behavior
SYSTEM_MESSAGE = (
    "You are a helpful Assistant. You let the user know if the information they share with you is true or false. "
    "You return the actual facts behind the information given, after stating the state of Truth or False."
)

# Function to handle chatbot responses
def respond(message, history):
    if history is None:
        history = []

    messages = [{"role": "system", "content": SYSTEM_MESSAGE}]
    
    for user, bot in history:
        messages.append({"role": "user", "content": user})
        messages.append({"role": "assistant", "content": bot})

    messages.append({"role": "user", "content": message})

    # ✅ Ensure streaming is disabled to prevent looping
    response = client.chat_completion(messages, max_tokens=200, temperature=0.7, stream=False)

    # ✅ Extract response only once
    bot_reply = response.choices[0].message.content.strip()

    # ✅ Append correctly formatted tuple to history
    history.append((message, bot_reply))

    # ✅ Return chatbot history and state
    return history, history

# Create Gradio chatbot UI
demo = gr.Interface(
    fn=respond,
    inputs=["text", "state"],  # ✅ Pass state properly
    outputs=["chatbot", "state"],  # ✅ Ensure state is included in outputs
    title="Misinformation Detection Chatbot",
    description="Ask anything, and the chatbot will verify whether it's true or false.",
    live=True,
)

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