app.py

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

# Load Fake News Detection Model
fake_news_pipeline = pipeline("text-classification", model="roberta-base-openai-detector")

# Function to classify text news
def classify_text(news_text):
    result = fake_news_pipeline(news_text)[0]
    label = result['label'].lower()
    score = result['score'] * 100  # Convert to percentage
    return ("Fake" if label == "fake" else "Real"), round(score, 2)

# Function to verify news with Google Fact Check API
def verify_news(news_text):
    search_url = f"https://toolbox.google.com/factcheck/explorer/search/{'+'.join(news_text.split())}"
    return search_url

# Function to analyze images for fake news
def analyze_image(image):
    transform = transforms.Compose([transforms.Resize((224, 224)), transforms.ToTensor()])
    image_tensor = transform(image).unsqueeze(0)

    # Convert to OpenCV format
    image_cv = np.array(image)
    image_cv = cv2.cvtColor(image_cv, cv2.COLOR_RGB2BGR)

    # Apply Fake Image Detection (Basic Example)
    edges = cv2.Canny(image_cv, 100, 200)
    edge_percentage = np.sum(edges) / (image_cv.shape[0] * image_cv.shape[1])

    if edge_percentage > 0.1:
        return "❌ This image might be **manipulated** or **deepfake**!"
    else:
        return "✅ This image appears to be **authentic**."

# Function to analyze video metadata
def analyze_video(video_url):
    api_url = f"https://noembed.com/embed?url={video_url}"
    response = requests.get(api_url).json()

    if "title" in response:
        title = response["title"]
        source = response["provider_name"]
        return f"🎥 Video Title: {title}\n🔗 Source: {source}"
    else:
        return "❌ Unable to fetch video details! Check if the link is correct."

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

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

# Input Sections
col1, col2, col3 = st.columns(3)

# Text Analysis Section
with col1:
    st.subheader("📄 Text News Check")
    news_text = st.text_area("Enter the news content:", height=200)
    if st.button("Analyze News"):
        if not news_text.strip():
            st.warning("Please enter some text.")
        else:
            result, accuracy = classify_text(news_text)
            verification_link = verify_news(news_text)

            if result == "Fake":
                st.error(f"❌ Likely **Fake News**! (Confidence: {accuracy}%)")
            else:
                st.success(f"✅ Likely **Real News**! (Confidence: {accuracy}%)")

            st.markdown(f"[🔎 Verify on Google Fact Check]({verification_link})")

# Image Upload Section
with col2:
    st.subheader("🖼️ Image News Check")
    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.image(image, caption="Uploaded Image", use_column_width=True)
        result = analyze_image(image)
        st.info(result)

# Video Link Section
with col3:
    st.subheader("🎥 Video News Check")
    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.video(video_url)
            result = analyze_video(video_url)
            st.info(result)