import os

import gradio as gr
import requests
from PIL import Image

from src.application.content_detection import NewsVerification
from src.application.url_reader import URLReader
from src.application.content_generation import generate_fake_image, generate_fake_text, replace_text


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

AZURE_TEXT_MODEL = ["gpt-4o-mini", "gpt-4o"]
AZURE_IMAGE_MODEL = ["dall-e-3", "Stable Diffusion (not supported)"]

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 generate_analysis_report(news_title:str, news_content: str, news_image: Image):
    news_analysis = NewsVerification()
    news_analysis.load_news(news_title, news_content, news_image)
    news_analysis.generate_analysis_report()
    return news_analysis.analyze_details()


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

    with gr.Row():
        # SETTINGS 
        with gr.Column(scale=1):
                with gr.Accordion("1. Enter a URL"):
                    url_input = gr.Textbox(
                        label="",
                        show_label=False,
                        value="",
                        )
                    load_button = gr.Button("Load URL")
                    
                with gr.Accordion("2. Select content-generation models", open=True, visible=False):
                    with gr.Row():
                            text_generation_model = gr.Dropdown(choices=AZURE_TEXT_MODEL, label="Text-generation model")
                            image_generation_model = gr.Dropdown(choices=AZURE_IMAGE_MODEL, label="Image-generation model")
                            generate_text_button = gr.Button("Generate text")
                            generate_image_button = gr.Button("Generate image")

                with gr.Accordion("3. Replace any terms", open=True, visible=False):
                    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.Accordion("Input News"):
                    news_title = gr.Textbox(label="Title", value="")
                    news_image = gr.Image(label="Image", type="filepath") 
                    news_content = gr.Textbox(label="Content", value="", lines=13)

        # NEWS ANALYSIS REPORT
        explanation = """
        - Green texts are the matched words in the input and source news.<br>
        - Each highlighted pair (marked with a number) shows the key differences between the input text and the source.
        """
        with gr.Column(scale=2):
            with gr.Accordion("News Analysis"):
                gr.HTML(explanation)
                detection_button = gr.Button("Verify news")
                detailed_analysis = gr.HTML("<br>"*40)

    # 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_text_button.click(generate_fake_text, 
                        inputs=[text_generation_model, news_title, news_content], 
                        outputs=[news_title, news_content])
    generate_image_button.click(generate_fake_image, 
                        inputs=[image_generation_model, news_title], 
                        outputs=[news_image])
    detection_button.click(generate_analysis_report,
                            inputs=[news_title, news_content, news_image],
                            outputs=[detailed_analysis]) 

    # change Image
    #url_input.change(load_image, inputs=url_input, outputs=image_view) 
    
    try:
        with open('examples/example_text_real.txt','r', encoding='utf-8') as file:
            text_real_1 = file.read()
        with open('examples/example_text_real_2.txt','r', encoding='utf-8') as file:
            text_real_2 = file.read()
        with open('examples/example_text_LLM_topic.txt','r', encoding='utf-8') as file:
            text_llm_topic = file.read()
        with open('examples/example_text_LLM_modification.txt','r', encoding='utf-8') as file:
            text_llm_modification = file.read()
        with open('examples/example_text_LLM_entities.txt','r', encoding='utf-8') as file:
            text_llm_entities = file.read()
    except FileNotFoundError:
        print("File not found.")
    except Exception as e:
        print(f"An error occurred: {e}")
    
    title_1 = "Southampton news: Leeds target striker Cameron Archer."
    title_2 = "Southampton news: Leeds target striker Cameron Archer."
    title_4 = "Japan pledges support for Ukraine with 100-year pact."
    
    image_1 = "examples/example_image_real_1.jpg.webp"
    image_2 = "examples/example_image_real_2.jpg.webp"
    image_3 = "examples/example_image_real_3.jpg"
    
    gr.Examples(
        examples=[
            [title_1, image_1, text_real_1 + '\n\n' + text_real_2],
            [title_1, image_2, text_real_1 + '\n\n' + text_llm_modification],
            [title_1, image_3, text_real_1 + '\n\n' + text_llm_topic],
            [title_4, image_3, text_llm_entities],
        ],
        inputs=[news_title, news_image, news_content],
        label="Examples",
        example_labels=[
            "2 real news",
            "1 real news + 1 LLM modification-based news",
            "1 real news + 1 LLM topic-based news",
            "1 LLM changed-entities news",
        ],
    )

demo.launch(share=False)