Update app.py

app.py

@@ -84,6 +84,134 @@
 
 # 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
 import cv2
@@ -100,11 +228,11 @@ 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")
+st.title("\U0001F4F0 Fake News & Deepfake Detection Tool")
+st.write("\U0001F680 Detect Fake News, Deepfake Images, and Videos using AI")
 
 # Load Models
-fake_news_detector = pipeline("text-classification", model="microsoft/deberta-v3-base")
+fake_news_detector = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
 # Load Deepfake Detection Models
 base_model_image = Xception(weights="imagenet", include_top=False)
@@ -138,32 +266,33 @@ def detect_deepfake_image(image_path):
     return {"label": label, "score": confidence}
 
 # ---- Fake News Detection Section ----
-st.subheader("📝 Fake News Detection")
+st.subheader("\U0001F4DD 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']
+    st.write("\U0001F50D Processing...")
+    labels = ["fake news", "real news"]
+    prediction = fake_news_detector(news_input, labels)
+    label = prediction['labels'][0]
+    confidence = prediction['scores'][0]
     
-    if label == "FAKE":
+    if label == "fake news":
         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")
+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="🖼️ Uploaded Image", use_column_width=True)
+    st.image(temp_file.name, caption="\U0001F5BC️ Uploaded Image", use_column_width=True)
     
     if st.button("Analyze Image"):
-        st.write("🔍 Processing...")
+        st.write("\U0001F50D Processing...")
         result = detect_deepfake_image(temp_file.name)
         
         if result["label"] == "FAKE":
@@ -172,7 +301,7 @@ if uploaded_image is not None:
             st.success(f"✅ Result: This image is Real. (Confidence: {1 - result['score']:.2f})")
 
 # ---- Deepfake Video Detection Section ----
-st.subheader("🎥 Deepfake Video Detection")
+st.subheader("\U0001F3A5 Deepfake Video Detection")
 uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
 
 def detect_deepfake_video(video_path):
@@ -202,7 +331,7 @@ if uploaded_video is not None:
         f.write(uploaded_video.read())
     
     if st.button("Analyze Video"):
-        st.write("🔍 Processing...")
+        st.write("\U0001F50D Processing...")
         result = detect_deepfake_video(temp_file.name)
         
         if result["label"] == "FAKE":
@@ -210,4 +339,4 @@ if uploaded_video is not None:
         else:
             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**")