Update app.py

app.py

@@ -1,9 +1,101 @@
+# 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")
@@ -11,75 +103,112 @@ 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:", "Type here...")
 
 if st.button("Check News"):
     st.write("🔍 Processing...")
-    st.success("✅ Result: This news is FAKE.")  # Replace with ML Model
+    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"])
 
-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)
+    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...")
-        st.error("⚠️ Result: This image is a Deepfake.")  # Replace with model
+        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 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
-
+def detect_deepfake_video(video_path):
     cap = cv2.VideoCapture(video_path)
+    frame_scores = []
     
-    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()
+        
+        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)
     
-    return temp_file.name
+    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)  # ✅ 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.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**")
+