Update app.py

app.py

@@ -84,157 +84,58 @@
 
 # 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
+import tempfile
+import os
+from PIL import Image
+import tensorflow as tf
 from transformers import pipeline
-import requests
+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 News & Deepfake Detection", layout="wide")
+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 Improved Fake News Detection Model
-fake_news_detector = pipeline("text-classification", model="roberta-base-openai-detector")
-
-# Fact-Checking API Function
-def fact_check_google(news_text):
-    api_url = f'https://factchecktools.googleapis.com/v1alpha1/claims:search?query={news_text}&key=YOUR_GOOGLE_FACTCHECK_API_KEY'
-    response = requests.get(api_url)
-    if response.status_code == 200:
-        data = response.json()
-        if "claims" in data:
-            return data["claims"]
-    return None
+# 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")
@@ -242,27 +143,71 @@ news_input = st.text_area("Enter News Text:", placeholder="Type here...")
 
 if st.button("Check News"):
     st.write("🔍 Processing...")
-    
-    # Step 1: AI-Based Classification
     prediction = fake_news_detector(news_input)
     label = prediction[0]['label']
     confidence = prediction[0]['score']
     
-    # Step 2: Fact Checking via API
-    fact_check_result = fact_check_google(news_input)
-    
     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)
     
-    # Display Fact Check Results
-    if fact_check_result:
-        st.write("📜 Fact Check Results:")
-        for claim in fact_check_result:
-            st.write(f"🔹 {claim['text']} - *{claim['claimReview'][0]['textualRating']}*")
-    else:
-        st.warning("⚠️ No Fact-Check Data Available.")
+    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**")
-