Update app.py

app.py

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+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=(100, 100))  # 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"] == "REAL":
+          st.success(f"✅ Result: This image is Real. (Confidence: {1 - result['score']:.2f})")
+        else: 
+            
+            st.error(f"⚠ Result: This image is a Deepfake. (Confidence: {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*")