import streamlit as st
import requests
from transformers import pipeline
from deepface import DeepFace
from PIL import Image
import cv2
import torch
import torchvision.transforms as transforms
import numpy as np
import os
import json

def load_text_model():
    return pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")

def classify_text(news_text, text_model):
    result = text_model(news_text)[0]
    label = result['label'].lower()
    score = result['score'] * 100
    return ("Fake" if label == "fake" else "Real"), round(score, 2)

def verify_news(news_text):
    sources = [
        "https://www.bbc.com/news",
        "https://www.cnn.com",
        "https://www.reuters.com",
        "https://factcheck.org",
        "https://www.snopes.com",
        "https://www.politifact.com"
    ]
    search_url = f"https://www.google.com/search?q={'+'.join(news_text.split())}"
    return sources, search_url

def analyze_image(image_path):
    try:
        result = DeepFace.analyze(image_path, actions=['age', 'gender', 'emotion'])
        return "Real", 85.0 if result else "Fake", 60.0
    except:
        return "Fake", 50.0

def analyze_video(video_path):
    cap = cv2.VideoCapture(video_path)
    frames = []
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        frames.append(frame)
    cap.release()
    return "Real", 80.0 if len(frames) > 10 else "Fake", 40.0

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

text_model = load_text_model()

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

if option == "Text":
    news_text = st.text_area("Enter the news content:")
    if st.button("Analyze News"):
        if news_text.strip():
            result, accuracy = classify_text(news_text, text_model)
            sources, verification_link = verify_news(news_text)
            st.write(f"Result: **{result}** (Accuracy: {accuracy}%)")
            for link in sources:
                st.markdown(f"[🔗 {link}]({link})")
            st.markdown(f"[🔎 Verify on Google]({verification_link})")
        else:
            st.warning("Please enter some text.")

elif option == "Image":
    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
    if uploaded_image:
        img = Image.open(uploaded_image)
        img_path = "uploaded_image.jpg"
        img.save(img_path)
        if st.button("Analyze Image"):
            result, accuracy = analyze_image(img_path)
            st.image(img, caption="Uploaded Image", use_column_width=True)
            st.write(f"Result: **{result}** (Accuracy: {accuracy}%)")
            os.remove(img_path)

elif option == "Video":
    uploaded_video = st.file_uploader("Upload a video", type=["mp4", "avi", "mov"])
    if uploaded_video:
        video_path = "uploaded_video.mp4"
        with open(video_path, "wb") as f:
            f.write(uploaded_video.read())
        if st.button("Analyze Video"):
            result, accuracy = analyze_video(video_path)
            st.video(video_path)
            st.write(f"Result: **{result}** (Accuracy: {accuracy}%)")
            os.remove(video_path)