import streamlit as st
import numpy as np
import os
os.system("pip install opencv-python-headless")
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("\U0001F4F0 Fake News & Deepfake Detection Tool")
st.write("\U0001F680 Detect Fake News, Deepfake Images, and Videos using AI")

# Load Fake News Detector
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
x = GlobalAveragePooling2D()(base_model_image.output)
x = Dense(1024, activation="relu")(x)
x = Dense(1, activation="sigmoid")(x)
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))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img /= 255.0
    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("\U0001F4DD Fake News Detection")
news_input = st.text_area("Enter News Text:", placeholder="Type here...")

if st.button("Check News"):
    st.write("\U0001F50D 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("✅ Result: This news appears legitimate.")

# ---- Deepfake Image Detection Section ----
st.subheader("\U0001F4F8 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="\U0001F5BC️ Uploaded Image", use_column_width=True)
    
    if st.button("Analyze Image"):
        st.write("\U0001F50D 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("\U0001F3A5 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("\U0001F50D 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("\U0001F4A1 **Developed for Fake News & Deepfake Detection Hackathon**")