# -*- coding: utf-8 -*-
"""app

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1ui1WeuYj77BJ2N4KZNWcxSgIwfOsWxFu
"""

# -*- coding: utf-8 -*-
"""Gannett Fact Checker"""

import os
import requests
import openai
import random
import gradio as gr
from openai import OpenAI

# Fetch your API keys securely from environment variables
openai_api_key = os.getenv("OPENAI_API_KEY")
serpapi_api_key = os.getenv("SERPAPI_API_KEY")

# Initialize the client and set the API key
client = OpenAI(api_key=openai_api_key)

# Function to get latest news using SerpAPI
def get_latest_news(topic):
    search_url = f"https://serpapi.com/search?api_key={serpapi_api_key}&q={topic}&tbm=nws"
    response = requests.get(search_url).json()
    articles = response.get('news_results', [])

    results = []
    for article in articles:
        results.append({
            'title': article.get('title'),
            'link': article.get('link')
        })

    return results

def add_confidence_score(is_fake, no_evidence, model_score):
    if no_evidence:
        confidence_score = round(random.uniform(0.0, 20.0), 2)  # Very low confidence for no evidence
        meter_status = "No Evidence"
    elif is_fake:
        confidence_score = round(random.uniform(0.0, 30.0), 2)  # Low confidence for fake
        meter_status = "Fake"
    else:
        # Use the model's score if not flagged as fake or lacking evidence
        confidence_score = model_score if model_score is not None else round(random.uniform(75.0, 99.0), 2)
        meter_status = "Real"

    return confidence_score, meter_status

def summarize_news_and_check_authenticity(news_articles, topic):
    if not news_articles:  # If there are no articles, handle it here
        return "No relevant articles were found for this topic.", 0.0, "No Evidence"

    # Prepare the prompt with the news articles
    articles_text = "\n".join([f"{article['title']}: {article['link']}" for article in news_articles])

    prompt = (f"Here are some news articles:\n{articles_text}\n\n"
              f"Based on the provided articles, evaluate whether they support or contradict the following topic: '{topic}'. "
              f"Summarize the content of these articles and determine if the topic is accurate or speculative. "
              f"Provide a confidence score in percentage (0 to 100%) reflecting how well the articles align with the topic. "
              f"Explain your reasoning for the confidence score given, and adjust the score according to how many articles support or contradict the topic. "
              f"A higher confidence score should be given if the majority of the articles align with the topic, and a lower confidence score if they do not.")

    # Call the OpenAI API for completion
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt}
        ]
    )

    result = response.choices[0].message.content

    # Extract confidence score from the response (if the model provides it)
    try:
        # Expecting confidence score to be presented as a percentage
        model_score = float(result.split("confidence score of ")[-1].split("%")[0].strip())
    except (IndexError, ValueError):
        model_score = None  # Default if the score isn't parsed correctly

    # Determine if the content is flagged as fake or if no evidence is found
    is_fake = "fake" in result.lower() or "not true" in result.lower() or "speculative" in result.lower()
    no_evidence = "no evidence" in result.lower() or "no articles" in result.lower()

    # Add confidence score and status based on the outcome
    confidence_score, meter_status = add_confidence_score(is_fake, no_evidence, model_score)

    # Create a clean, unformatted result
    result_display = (
        f"Summary and Authenticity Check:\n{result}"
    )

    return result_display, confidence_score, meter_status


# Function to combine everything for Gradio UI
def get_news_and_check_authenticity(topic):
    news_results = get_latest_news(topic)

    # Get summary and authenticity check
    summary_and_authenticity, confidence_score, meter_status = summarize_news_and_check_authenticity(news_results, topic)

    # Create a formatted string to show the results
    news_display = "\n".join([f"Title: {article['title']} \nLink: {article['link']}" for article in news_results])
    result_display = (
        f"News Results:\n{news_display}\n\n"
        f"{summary_and_authenticity}\n\n"
        f"Confidence Score: {confidence_score:.2f}%\n"
        f"Fact Check: {meter_status}"
    )

    return result_display

# Create the Gradio interface
interface = gr.Interface(
    fn=get_news_and_check_authenticity,
    inputs="text",  # Users will input the topic in a text box
    outputs="text",  # The result will be displayed as text
    title="Fact Checker",
    description="Enter a topic to check the latest news and verify its authenticity."
)

# Launch the Gradio interface
interface.launch(share=True)