application.py

import os

import gradio as gr
import openai
import requests
from PIL import Image
import re

from src.application.content_detection import generate_analysis_report
from src.application.url_reader import URLReader
from src.application.content_generation import generate_content, replace_text

# from dotenv import load_dotenv

# load_dotenv()
# AZURE_OPENAI_API_KEY = os.getenv('AZURE_OPENAI_API_KEY')
# AZURE_OPENAI_ENDPOINT = os.getenv('AZURE_OPENAI_ENDPOINT')
# AZURE_OPENAI_API_VERSION = os.getenv('AZURE_OPENAI_API_VERSION')

# client = openai.AzureOpenAI(
#     api_version = AZURE_OPENAI_API_VERSION,
#     api_key = AZURE_OPENAI_API_KEY,  
#     azure_endpoint = AZURE_OPENAI_ENDPOINT,
#     )

GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
SEARCH_ENGINE_ID = os.getenv('SEARCH_ENGINE_ID')

AZURE_OPENAI_MODEL = ["gpt-4o-mini", "gpt-4o"]

def load_url(url):
    """
    Load content from the given URL.
    """
    content = URLReader(url)
    image = None
    header = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36'}
    try:
        response = requests.get(
            url, 
            headers = header,
            stream = True
        )
        response.raise_for_status()  # Raise an exception for bad status codes
        
        image_response = requests.get(content.top_image, stream=True)
        try:
            image = Image.open(image_response.raw)
        except:
            print(f"Error loading image from {content.top_image}")
            
    except (requests.exceptions.RequestException, FileNotFoundError) as e:
        print(f"Error fetching image: {e}")

    return content.title, content.text, image

def show_detailed_analysis(title):
    return f"More details of {title} will be shown here."

# Define the GUI
with gr.Blocks() as demo:
    gr.Markdown("# FAKE NEWS DETECTION")

    with gr.Row():
        # SETTINGS 
        with gr.Column(scale=1):
            with gr.Accordion("Settings"):
                gr.Markdown("This tool generates fake news by modifying the content of a given URL.")

                with gr.Accordion("1. Enter a URL"):
                    url_input = gr.Textbox(
                        label="URL",
                        value="https://bbc.com/future/article/20250110-how-often-you-should-wash-your-towels-according-to-science",
                        )
                    load_button = gr.Button("Load URL")
                    
                with gr.Accordion("2. Select a content-generation model", open=True):
                    with gr.Row():
                        text_generation_model = gr.Dropdown(choices=AZURE_OPENAI_MODEL, label="Text-generation model")
                        image_generation_model = gr.Dropdown(choices=["Dall-e", "Stable Diffusion"], label="Image-generation model")
                    generate_button = gr.Button("Random generation")

                with gr.Accordion("3. Replace any terms", open=True):
                    replace_df = gr.Dataframe(
                        headers=["Find what:", "Replace with:"],
                        datatype=["str", "str"],
                        row_count=(1, "dynamic"),
                        col_count=(2, "fixed"),
                        interactive=True
                    )
                    replace_button = gr.Button("Replace all")

        # GENERATED CONTENT
        with gr.Column(scale=1):
            with gr.Accordion("Generated News Contents"):
                detection_button = gr.Button("Check for fake news")
                news_title = gr.Textbox(label="Title", value="")
                news_image = gr.Image(label="Image") 
                news_content = gr.Textbox(label="Content", value="", lines=12)

        # FAKE NEWS ANALYSIS REPORT
        with gr.Column(scale=1):
            with gr.Accordion("Fake News Analysis"):
                html_out = gr.HTML() 
                detailed_analysis_button = gr.Button("Show detailed analysis...")

    # Connect events
    load_button.click(
        load_url, 
        inputs=url_input, 
        outputs=[news_title, news_content, news_image]
        )
    replace_button.click(replace_text, 
                        inputs=[news_title, news_content, replace_df], 
                        outputs=[news_title, news_content])
    generate_button.click(generate_content, 
                        inputs=[text_generation_model, image_generation_model, news_title, news_content], 
                        outputs=[news_title, news_content])
    detection_button.click(generate_analysis_report,
                           inputs=[news_title, news_content, news_image],
                           outputs=html_out) 
    detailed_analysis_button.click(show_detailed_analysis,
                                   inputs=[news_title],
                                   outputs=[html_out])
    # change Image
    #url_input.change(load_image, inputs=url_input, outputs=image_view) 

demo.launch()