app.py

# import streamlit as st
# import numpy as np
# import cv2
# import tempfile
# import os
# from PIL import Image

# # ---- Page Configuration ----
# st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide")

# st.title("📰 Fake News & Deepfake Detection Tool")
# st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI")

# # ---- Fake News Detection Section ----
# st.subheader("📝 Fake News Detection")
# news_input = st.text_area("Enter News Text:", "Type here...")

# if st.button("Check News"):
#     st.write("🔍 Processing...")
#     st.success("✅ Result: This news is FAKE.")  # Replace with ML Model

# # ---- Deepfake Image Detection Section ----
# st.subheader("📸 Deepfake Image Detection")
# uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])

# def compress_image(image, quality=90, max_size=(300, 300)):  # ✅ High clarity image
#     img = Image.open(image).convert("RGB")
#     img.thumbnail(max_size)  
#     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
#     img.save(temp_file.name, "JPEG", quality=quality)
#     return temp_file.name

# if uploaded_image is not None:
#     compressed_image_path = compress_image(uploaded_image)
#     st.image(compressed_image_path, caption="🖼️ Compressed & Clear Image", use_column_width=True)
#     if st.button("Analyze Image"):
#         st.write("🔍 Processing...")
#         st.error("⚠️ Result: This image is a Deepfake.")  # Replace with model

# # ---- Deepfake Video Detection Section ----
# st.subheader("🎥 Deepfake Video Detection")
# uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])

# def compress_video(video):
#     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")

#     with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_video:
#         temp_video.write(video.read())
#         video_path = temp_video.name

#     cap = cv2.VideoCapture(video_path)
    
#     if not cap.isOpened():
#         st.error("❌ Error: Unable to read video!")
#         return None

#     fourcc = cv2.VideoWriter_fourcc(*'mp4v')

#     # ✅ New Resolution (100x80) & 15 FPS
#     frame_width = 50  
#     frame_height = 80  
#     out = cv2.VideoWriter(temp_file.name, fourcc, 15.0, (frame_width, frame_height))  

#     while cap.isOpened():
#         ret, frame = cap.read()
#         if not ret:
#             break
#         frame = cv2.resize(frame, (frame_width, frame_height))
#         out.write(frame)

#     cap.release()
#     out.release()
    
#     return temp_file.name

# if uploaded_video is not None:
#     st.video(uploaded_video)  # ✅ فوراً ویڈیو اپ لوڈ ہونے کے بعد دکھائیں
#     compressed_video_path = compress_video(uploaded_video)
#     if compressed_video_path:
#         st.video(compressed_video_path)  # ✅ کمپریسڈ ویڈیو بھی دکھائیں
#         if st.button("Analyze Video"):
#             st.write("🔍 Processing...")
#             st.warning("⚠️ Result: This video contains Deepfake elements.")  # Replace with model

# st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")

# import streamlit as st
# import numpy as np
# import cv2
# import tempfile
# import os
# from PIL import Image
# import tensorflow as tf
# from transformers import pipeline
# from tensorflow.keras.applications import Xception, EfficientNetB7
# from tensorflow.keras.models import Model
# from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
# from tensorflow.keras.preprocessing.image import load_img, img_to_array

# # ---- Page Configuration ----
# st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide")

# st.title("📰 Fake News & Deepfake Detection Tool")
# st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI")

# # Load Models
# fake_news_detector = pipeline("text-classification", model="microsoft/deberta-v3-base")

# # Load Deepfake Detection Models
# base_model_image = Xception(weights="imagenet", include_top=False)
# base_model_image.trainable = False  # Freeze base layers
# x = GlobalAveragePooling2D()(base_model_image.output)
# x = Dense(1024, activation="relu")(x)
# x = Dense(1, activation="sigmoid")(x)  # Sigmoid for probability output
# deepfake_image_model = Model(inputs=base_model_image.input, outputs=x)

# base_model_video = EfficientNetB7(weights="imagenet", include_top=False)
# base_model_video.trainable = False
# x = GlobalAveragePooling2D()(base_model_video.output)
# x = Dense(1024, activation="relu")(x)
# x = Dense(1, activation="sigmoid")(x)
# deepfake_video_model = Model(inputs=base_model_video.input, outputs=x)

# # Function to Preprocess Image
# def preprocess_image(image_path):
#     img = load_img(image_path, target_size=(299, 299))  # Xception expects 299x299
#     img = img_to_array(img)
#     img = np.expand_dims(img, axis=0)
#     img /= 255.0  # Normalize pixel values
#     return img

# # Function to Detect Deepfake Image
# def detect_deepfake_image(image_path):
#     image = preprocess_image(image_path)
#     prediction = deepfake_image_model.predict(image)[0][0]
#     confidence = round(float(prediction), 2)
#     label = "FAKE" if confidence > 0.5 else "REAL"
#     return {"label": label, "score": confidence}

# # ---- Fake News Detection Section ----
# st.subheader("📝 Fake News Detection")
# news_input = st.text_area("Enter News Text:", placeholder="Type here...")

# if st.button("Check News"):
#     st.write("🔍 Processing...")
#     prediction = fake_news_detector(news_input)
#     label = prediction[0]['label']
#     confidence = prediction[0]['score']
    
#     if label == "FAKE":
#         st.error(f"⚠️ Result: This news is FAKE. (Confidence: {confidence:.2f})")
#     else:
#         st.success(f"✅ Result: This news is REAL. (Confidence: {confidence:.2f})")

# # ---- Deepfake Image Detection Section ----
# st.subheader("📸 Deepfake Image Detection")
# uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])

# if uploaded_image is not None:
#     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
#     img = Image.open(uploaded_image).convert("RGB")
#     img.save(temp_file.name, "JPEG")
#     st.image(temp_file.name, caption="🖼️ Uploaded Image", use_column_width=True)
    
#     if st.button("Analyze Image"):
#         st.write("🔍 Processing...")
#         result = detect_deepfake_image(temp_file.name)
        
#         if result["label"] == "FAKE":
#             st.error(f"⚠️ Result: This image is a Deepfake. (Confidence: {result['score']:.2f})")
#         else:
#             st.success(f"✅ Result: This image is Real. (Confidence: {1 - result['score']:.2f})")

# # ---- Deepfake Video Detection Section ----
# st.subheader("🎥 Deepfake Video Detection")
# uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])

# def detect_deepfake_video(video_path):
#     cap = cv2.VideoCapture(video_path)
#     frame_scores = []
    
#     while cap.isOpened():
#         ret, frame = cap.read()
#         if not ret:
#             break
        
#         frame_path = "temp_frame.jpg"
#         cv2.imwrite(frame_path, frame)
#         result = detect_deepfake_image(frame_path)
#         frame_scores.append(result["score"])
#         os.remove(frame_path)
    
#     cap.release()
#     avg_score = np.mean(frame_scores)
#     final_label = "FAKE" if avg_score > 0.5 else "REAL"
#     return {"label": final_label, "score": round(float(avg_score), 2)}

# if uploaded_video is not None:
#     st.video(uploaded_video)
#     temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
#     with open(temp_file.name, "wb") as f:
#         f.write(uploaded_video.read())
    
#     if st.button("Analyze Video"):
#         st.write("🔍 Processing...")
#         result = detect_deepfake_video(temp_file.name)
        
#         if result["label"] == "FAKE":
#             st.warning(f"⚠️ Result: This video contains Deepfake elements. (Confidence: {result['score']:.2f})")
#         else:
#             st.success(f"✅ Result: This video is Real. (Confidence: {1 - result['score']:.2f})")

# st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")

import streamlit as st
import numpy as np
from transformers import pipeline
import requests

# ---- Page Configuration ----
st.set_page_config(page_title="Fake News & Deepfake Detection", layout="wide")

st.title("📰 Fake News & Deepfake Detection Tool")
st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI")

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

# Fact-Checking API Function
def fact_check_google(news_text):
    api_url = f'https://factchecktools.googleapis.com/v1alpha1/claims:search?query={news_text}&key=YOUR_GOOGLE_FACTCHECK_API_KEY'
    response = requests.get(api_url)
    if response.status_code == 200:
        data = response.json()
        if "claims" in data:
            return data["claims"]
    return None

# ---- Fake News Detection Section ----
st.subheader("📝 Fake News Detection")
news_input = st.text_area("Enter News Text:", placeholder="Type here...")

if st.button("Check News"):
    st.write("🔍 Processing...")
    
    # Step 1: AI-Based Classification
    prediction = fake_news_detector(news_input)
    label = prediction[0]['label']
    confidence = prediction[0]['score']
    
    # Step 2: Fact Checking via API
    fact_check_result = fact_check_google(news_input)
    
    if label == "FAKE":
        st.error(f"⚠️ Result: This news is FAKE. (Confidence: {confidence:.2f})")
    else:
        st.success(f"✅ Result: This news is REAL. (Confidence: {confidence:.2f})")
    
    # Display Fact Check Results
    if fact_check_result:
        st.write("📜 Fact Check Results:")
        for claim in fact_check_result:
            st.write(f"🔹 {claim['text']} - *{claim['claimReview'][0]['textualRating']}*")
    else:
        st.warning("⚠️ No Fact-Check Data Available.")

st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")