import gradio as gr
import tweepy
from groq import Groq
import os

api_key = os.getenv("Groqapi")

# Define the Groq-based function to predict fake news
def predict_fake_news(text):
    client = Groq(api_key= api_key)
    completion = client.chat.completions.create(
        model="llama-3.1-70b-versatile",
        messages=[
            {
                "role": "system",
                "content": "You are an experienced and up-to-date fact-checker who is very proficient in identifying falsehood and has a repertoire of knowledge of what real news is and what fake news is. You have over 30 years of experience in the field. I want you to analyse any news tweet entered and reply with ONLY one word 'Fake' if it is fake and 'Real' if it is real."
            },
            {
                "role": "user",
                "content": text
            }
        ],
        temperature=1,
        max_tokens=8000,
        top_p=1,
        stream=True,
        stop=None,
    )
    
    # Iterate over the streaming response to get the result
    prediction = ""
    for chunk in completion:
        prediction += chunk.choices[0].delta.content or ""
    
    return prediction.strip()  # Return the result (Fake or Real)

# Define a function to update on Twitter
def update_on_Twitter(tweet_text, prediction):
    # Replace with your own Twitter API credentials
    CONSUMER_KEY = os.getenv("TwitterConsumer")
    CONSUMER_SECRET = os.getenv("ConsumerSecret")
    ACCESS_TOKEN = os.getenv("AccessToken")
    ACCESS_TOKEN_SECRET = os.getenv("AccTokenSecret")
    BEARER_TOKEN = os.getenv("BearerToken")

    # Authenticate to Twitter
    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)
    auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)

    # Create an API object
    api = tweepy.API(auth)

    # Create a Client object for posting tweets
    client = tweepy.Client(
      BEARER_TOKEN,
      CONSUMER_KEY,
      CONSUMER_SECRET,
      ACCESS_TOKEN,
      ACCESS_TOKEN_SECRET,
      wait_on_rate_limit=True
    )

    post_text = f"The news: {tweet_text} is {prediction}."

    try:
        api.verify_credentials()
        print("Authentication OK")
        client.create_tweet(text=post_text)
        return f'<a href="https://twitter.com/CANNBot" target="_blank">Detect Fake News on Twitter Bot Account</a>'
    except Exception as e:
        print(e)
        return f'Error: {e}'

# Use Gradio Blocks to create a more flexible interface
with gr.Blocks() as demo:
    gr.Markdown("# Fake News Detection that updates on X")
    text_input = gr.Textbox(placeholder="Enter a news Tweet here...", label="News Tweet")
    text_output = gr.Textbox(label="Prediction")
    link_output = gr.HTML(label="Twitter Bot Account")

    # Button to get prediction using Groq LLM
    gr.Button("Detect").click(predict_fake_news, inputs=text_input, outputs=text_output)

    # Button to generate a Gradio link and post to Twitter
    gr.Button("Detect on Twitter").click(update_on_Twitter, inputs=[text_input, text_output], outputs=link_output)

# Launch the interface               
demo.launch()