Update app.py

app.py

@@ -1,89 +1,3 @@
-# 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
@@ -321,56 +235,7 @@ if uploaded_image is not None:
         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 = []
-#     frame_count = 0
-    
-#     while cap.isOpened():
-#         ret, frame = cap.read()
-#         if not ret:
-#             break
-        
-#         if frame_count % 10 == 0:  # ہر 10ویں فریم کا تجزیہ کریں
-#             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)
-        
-#         frame_count += 1
-    
-#     cap.release()
-    
-#     if not frame_scores:
-#         return {"label": "UNKNOWN", "score": 0.0}  # اگر کوئی فریم پراسیس نہ ہو سکے
-    
-#     avg_score = np.mean(frame_scores)
-#     confidence = round(float(avg_score), 2)
-#     final_label = "FAKE" if avg_score > 0.5 else "REAL"
-    
-#     return {"label": final_label, "score": confidence}
-
-# 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... Please wait.")
-#         result = detect_deepfake_video(temp_file.name)
-        
-#         if result["label"] == "FAKE":
-#             st.error(f"⚠️ Deepfake Detected! This video appears to be FAKE. (Confidence: {result['score']:.2f})")
-#         elif result["label"] == "REAL":
-#             st.success(f"✅ This video appears to be REAL. (Confidence: {1 - result['score']:.2f})")
-#         else:
-#             st.warning("⚠️ Unable to analyze the video. Please try a different file.")
+ # ---- Deepfake Video Detection Section ----
 st.subheader("🎥 Deepfake Video Detection")
 
 # Upload video file