import streamlit as st
from transformers import pipeline
from deep_translator import GoogleTranslator
import requests
############ SETTING UP THE PAGE LAYOUT AND TITLE ############

# `st.set_page_config` is used to display the default layout width, the title of the app, and the emoticon in the browser tab.

st.set_page_config(layout="centered", page_title="FND Fake News")

############ CREATE THE LOGO AND HEADING ############

# We create a set of columns to display the logo and the heading next to each other.
c1, c2 = st.columns([0.32, 2])

# The snowflake logo will be displayed in the first column, on the left.
with c1:
    st.caption("")
    st.title("📑")

# The heading will be on the right.
with c2:
    st.caption("")
    st.title("FND Detection")

# We need to set up session state via st.session_state so that app interactions don't reset the app.
if "valid_inputs_received" not in st.session_state:
    st.session_state["valid_inputs_received"] = False

############ SIDEBAR CONTENT ############

st.sidebar.subheader("Model Options")
st.sidebar.write("")
SELECTED_MODEL = st.sidebar.selectbox("Choose a model", ("XLm-Roberta", "Lstm"))

LANGUAGE = st.sidebar.selectbox("Language", ("en", "ar"), format_func=lambda x: "English" if x == "en" else "Arabic")

# Model selection

if SELECTED_MODEL:
    st.session_state.valid_inputs_received = False

MODEL_INFO = {
    "XLm-Roberta": """
    
    This model is trained by over 400,000 arabic news and 70000 english news from different medias based on the 'XLm-roberta-base'. It can give result by simply entering the text of the news less than 90000 words(the excess will be truncated automatically).
    """,
    "Lstm": """
   
    It is trained on the provided datasets\n
    
    """,
    None: "NO MODEL SELECTED",
}


model_info_container = st.sidebar.container(border=True)
model_info_container.markdown(MODEL_INFO[SELECTED_MODEL])


copyright_container = st.sidebar.container(border=True)
copyright_container.markdown("Copyright ©️ 2024 [Mohamed Ed Deryouch](https://huggingface.co/edderyouch)")


############ TABBED NAVIGATION ############


MainTab , InfoTab = st.tabs(["Main","Info"])

############ TRANSLATION FUNCTIONS ############

def translate_to_english(text):
    try:
        if text.strip():  # Only translate if there's actual text
            return GoogleTranslator(source="auto", target="en").translate(text)
        return text
    except Exception as e:
        st.error(f"Translation error: {str(e)}")
        return text

def translate_from_english(text, target_language):
    if target_language == "en" or not text.strip():
        return text
    try:
        return GoogleTranslator(source="en", target=target_language).translate(text)
    except Exception as e:
        st.error(f"Translation error: {str(e)}")
        return text
############ API VERIFICATION FUNCTIONS ############
NEWS_API_KEY = 'f939987352f04e96b798ca181910c2f0'
NEWS_API_URL = 'https://newsapi.org/v2/everything'
FACT_CHECK_API_KEY = "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"
def verify_with_news_api(query):
    if not NEWS_API_KEY:
        return None
    
    url = f"https://factchecktools.googleapis.com/v1alpha1/claims:search?query={query}&key={FACT_CHECK_API_KEY}"
    response = requests.get(url)
    print(response)
    if response.status_code == 200:
        claims = response.json().get("claims", [])
        return any(claim["claimReview"] for claim in claims)
    return False



############ MODEL FUNCTION ############

def MODEL_RESULT(model: str, news: str) -> str | None:
    if model == "XLm-Roberta":
        MODEL_jy46604790 = "jy46604790/Fake-News-Bert-Detect"
        classifier = pipeline(
            "text-classification", model=MODEL_jy46604790, tokenizer=MODEL_jy46604790
        )
        result = classifier(news)

        if result[0]["label"] == "LABEL_1":
            return "REAL NEWS"
        else:
            return "FAKE NEWS"



    if model == "Lstm":
        from utils import modelx

        return modelx(arch=model, model_path="models/lstm/x_g85_lstm.keras", text=news)


with MainTab:
    st.write("")
    st.markdown("Classify News based on the selected ml model.")
    st.write("")
    
    container = st.container(border=True)
    container.write(f"Selected model: {SELECTED_MODEL}")
    container.write(f"Selected language: {'English' if LANGUAGE == 'en' else 'Arabic'}")

    with st.form(key="main_form"):
        # Predefined news examples
        pre_defined_news = {
            "en": "SCIENTISTS DISCOVER TALKING ELEPHANTS: Researchers at Harvard",
            "ar": " هبطت مركبة ناسا الجوالة 'بيرسيفيرانس' بنجاح على سطح المريخ "
        }
        
        news = st.text_area(
            "Enter news to classify",
            pre_defined_news[LANGUAGE],
            height=200,
            help="Please provide the news that you need to verify for its truthfulness.",
            key="news",
        )

        submit_button = st.form_submit_button(label="Analyze News")

    if submit_button and not news.strip():
        st.warning("📑 Please enter some news text to analyze")
        st.stop()
    elif submit_button or st.session_state.valid_inputs_received:
        if submit_button:
            st.session_state.valid_inputs_received = True
        
        # Translation process
        processed_news = news

        
        processed_news = translate_to_english(news) if LANGUAGE == "ar" else news
    
    # Get model prediction
        with st.spinner("Analyzing..."):
            prediction = MODEL_RESULT(SELECTED_MODEL, processed_news)

            query = " ".join(processed_news.split()[:5])  # Use first 5 words as query
            api_result = verify_with_news_api(query)

        print(prediction)
        st.subheader("Result")
        if prediction == "REAL NEWS":
            st.success("✅ Model Prediction: REAL NEWS")
        else:
            st.error("❌ Model Prediction: FAKE NEWS")