import streamlit as st
import requests
from transformers import pipeline
from PIL import Image
import torch
import torchvision.transforms as transforms
import io

# Load Fake News Detection Model from Hugging Face
fake_news_pipeline = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")

def classify_text(news_text):
    result = fake_news_pipeline(news_text)[0]['label'].lower()
    return "Fake" if result == "fake" else "Real"

def analyze_image(image):
    # Convert image to tensor (Placeholder: Model should be updated with a real image classifier)
    transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
    image_tensor = transform(image).unsqueeze(0)
    return "Image analysis feature coming soon!"

def verify_news(news_text):
    search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
    return search_url

# Streamlit UI
st.set_page_config(page_title="Fake News Detector", layout="wide")
st.title("📰 Fake News Detector")

# Tabs for Input and Results
tab1, tab2 = st.tabs(["Input", "Results"])

with tab1:
    st.sidebar.title("Select Input Type")
    option = st.sidebar.radio("Choose an option", ["Text", "Image", "Video Link"])

    if option == "Text":
        news_text = st.text_area("Enter the news content to check:", height=200)
        if st.button("Analyze News"):
            if not news_text.strip():
                st.warning("Please enter some text.")
            else:
                st.session_state["news_text"] = news_text
                st.session_state["analyze_text"] = True
                st.experimental_rerun()

    elif option == "Image":
        uploaded_image = st.file_uploader("Upload a news image", type=["jpg", "png", "jpeg"])
        if uploaded_image and st.button("Analyze Image"):
            image = Image.open(uploaded_image)
            st.session_state["news_image"] = image
            st.session_state["analyze_image"] = True
            st.experimental_rerun()

    elif option == "Video Link":
        video_url = st.text_input("Enter the video link:")
        if st.button("Analyze Video"):
            if not video_url.strip():
                st.warning("Please enter a valid video link.")
            else:
                st.session_state["video_url"] = video_url
                st.session_state["analyze_video"] = True
                st.experimental_rerun()

with tab2:
    if st.session_state.get("analyze_text", False):
        news_text = st.session_state.get("news_text", "")
        with st.spinner("Analyzing text..."):
            result = classify_text(news_text)
            verification_link = verify_news(news_text)
            
            if result == "Fake":
                st.error("❌ This news is likely **Fake**!", icon="⚠️")
            else:
                st.success("✅ This news is likely **Real**!", icon="✅")
            
            st.subheader("🔍 Verification & Trusted Sources")
            sources = ["https://www.bbc.com/news", "https://www.cnn.com", "https://www.reuters.com"]
            for link in sources:
                st.markdown(f"[🔗 {link}]({link})")
            st.markdown(f"[🔎 Verify on Google]({verification_link})")

    if st.session_state.get("analyze_image", False):
        image = st.session_state.get("news_image")
        st.image(image, caption="Uploaded Image", use_column_width=True)
        st.info(analyze_image(image))

    if st.session_state.get("analyze_video", False):
        video_url = st.session_state.get("video_url", "")
        st.video(video_url)
        st.info("Video analysis feature coming soon!")