Update app.py

app.py

@@ -103,68 +103,54 @@ 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 Fake News Detector ----
-try:
-    fake_news_detector = pipeline("text-classification", model="microsoft/deberta-v3-base")
-except Exception as e:
-    st.error(f"Error loading fake news model: {e}")
-    fake_news_detector = None
-
-# ---- Load Deepfake Detection Models ----
-try:
-    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)
-except Exception as e:
-    st.error(f"Error loading image model: {e}")
-    deepfake_image_model = None
-
-try:
-    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)
-except Exception as e:
-    st.error(f"Error loading video model: {e}")
-    deepfake_video_model = None
-
-# ---- Image Preprocessing Function ----
+# 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):
-    try:
-        img = load_img(image_path, target_size=(299, 299))  # Xception requires 299x299
-        img = img_to_array(img)
-        img = np.expand_dims(img, axis=0)
-        img /= 255.0  # Normalize
-        return img
-    except Exception as e:
-        st.error(f"Error processing image: {e}")
-        return None
+    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"):
-    if not news_input.strip():
-        st.warning("⚠️ Please enter news text before checking.")
-    elif fake_news_detector:
-        st.write("🔍 Processing...")
-        prediction = fake_news_detector(news_input)
-        label = prediction[0]['label']
-        confidence = prediction[0]['score']
-
-        if label.lower() == "fake":
-            st.error(f"⚠️ Result: This news is FAKE. (Confidence: {confidence:.2f})")
-        else:
-            st.success(f"✅ Result: This news is REAL. (Confidence: {confidence:.2f})")
+    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.error("Fake news detection model not loaded.")
-
+        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"])
@@ -174,22 +160,16 @@ if uploaded_image is not None:
     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"):
-        if deepfake_image_model:
-            st.write("🔍 Processing...")
-            image_data = preprocess_image(temp_file.name)
-            if image_data is not None:
-                prediction = deepfake_image_model.predict(image_data)[0][0]
-                confidence = round(float(prediction), 2)
-                label = "FAKE" if confidence > 0.5 else "REAL"
-
-                if label == "REAL":
-                    st.success(f"✅ Result: This image is Real. (Confidence: {1 - confidence:.2f})")
-                else:
-                    st.error(f"⚠️ Result: This image is a Deepfake. (Confidence: {confidence:.2f})")
-        else:
-            st.error("Deepfake image detection model not loaded.")
+        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")
@@ -199,28 +179,18 @@ def detect_deepfake_video(video_path):
     cap = cv2.VideoCapture(video_path)
     frame_scores = []
     
-    if not cap.isOpened():
-        st.error("Error: Cannot open video file.")
-        return None
-
     while cap.isOpened():
         ret, frame = cap.read()
         if not ret:
             break
-
+        
         frame_path = "temp_frame.jpg"
         cv2.imwrite(frame_path, frame)
-        processed_image = preprocess_image(frame_path)
-        
-        if processed_image is not None:
-            prediction = deepfake_image_model.predict(processed_image)[0][0]
-            frame_scores.append(prediction)
+        result = detect_deepfake_image(frame_path)
+        frame_scores.append(result["score"])
         os.remove(frame_path)
     
     cap.release()
-    if not frame_scores:
-        return None
-
     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)}
@@ -230,19 +200,14 @@ if uploaded_video is not None:
     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"):
-        if deepfake_video_model:
-            st.write("🔍 Processing...")
-            result = detect_deepfake_video(temp_file.name)
-
-            if result is None:
-                st.error("⚠️ Unable to analyze video.")
-            elif 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.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.error("Deepfake video detection model not loaded.")
+            st.success(f"✅ Result: This video is Real. (Confidence: {1 - result['score']:.2f})")
 
-st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")
+st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")