Update app.py

app.py

@@ -1,127 +1,44 @@
-# import streamlit as st
-# import numpy as np
-# import cv2
-# import tempfile
-# import os
-
-# # ---- 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...")
-#     # Fake news detection logic (Placeholder)
-#     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"])
-
-# if uploaded_image is not None:
-#     st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
-#     if st.button("Analyze Image"):
-#         st.write("🔍 Processing...")
-#         # Deepfake detection logic (Placeholder)
-#         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"])
-
-# if uploaded_video is not None:
-#     st.video(uploaded_video)
-#     if st.button("Analyze Video"):
-#         st.write("🔍 Processing...")
-#         # Deepfake video detection logic (Placeholder)
-#         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 cv2
 import numpy as np
+import cv2
 import tempfile
 import os
-from PIL import Image
-
-st.set_page_config(page_title="Fake News & Deepfake Detection", layout="wide")
-
-# 👉 Image Compression Function
-def compress_image(image, quality=20, max_size=(500, 500)):
-    img = Image.open(image).convert("RGB")
-    img.thumbnail(max_size)  # Resize while keeping aspect ratio
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
-    img.save(temp_file.name, "JPEG", quality=quality)
-    return temp_file.name
-
-# 👉 Video Compression Function
-def compress_video(video):
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
-
-    # ✅ Save video to temporary file
-    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')
+# ---- Page Configuration ----
+st.set_page_config(page_title="Fake & Deepfake Detection", layout="wide")
 
-    # ✅ Reduce resolution to 480p
-    frame_width = 640  
-    frame_height = 480  
-    out = cv2.VideoWriter(temp_file.name, fourcc, 20.0, (frame_width, frame_height))
+st.title("📰 Fake News & Deepfake Detection Tool")
+st.write("🚀 Detect Fake News, Deepfake Images, and Videos using AI")
 
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        frame = cv2.resize(frame, (frame_width, frame_height))
-        out.write(frame)
+# ---- Fake News Detection Section ----
+st.subheader("📝 Fake News Detection")
+news_input = st.text_area("Enter News Text:", "Type here...")
 
-    cap.release()
-    out.release()
-    
-    return temp_file.name
+if st.button("Check News"):
+    st.write("🔍 Processing...")
+    # Fake news detection logic (Placeholder)
+    st.success("✅ Result: This news is FAKE.")  # Replace with ML Model
 
-# 👉 Streamlit UI
-st.title("🕵️‍♂️ Fake News & Deepfake Detection Tool")
+# ---- Deepfake Image Detection Section ----
+st.subheader("📸 Deepfake Image Detection")
+uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "png", "jpeg"])
 
-st.sidebar.header("📂 Upload Your File")
-option = st.sidebar.radio("Select File Type", ["📷 Image", "📹 Video", "📝 Text"])
+if uploaded_image is not None:
+    st.image(uploaded_image, caption="Uploaded Image", use_column_width=True)
+    if st.button("Analyze Image"):
+        st.write("🔍 Processing...")
+        # Deepfake detection logic (Placeholder)
+        st.error("⚠️ Result: This image is a Deepfake.")  # Replace with model
 
-# 👉 Image Upload & Compression
-if option == "📷 Image":
-    uploaded_file = st.sidebar.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
-    if uploaded_file:
-        compressed_path = compress_image(uploaded_file)
-        image = Image.open(compressed_path)
-        st.image(image, caption="🖼️ Compressed Image", use_column_width=True)
-        st.success("✅ Image uploaded and compressed successfully!")
+# ---- Deepfake Video Detection Section ----
+st.subheader("🎥 Deepfake Video Detection")
+uploaded_video = st.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
 
-# 👉 Video Upload & Compression
-elif option == "📹 Video":
-    uploaded_file = st.sidebar.file_uploader("Upload a Video", type=["mp4", "avi", "mov"])
-    if uploaded_file:
-        compressed_path = compress_video(uploaded_file)
-        if compressed_path:
-            st.video(compressed_path)
-            st.success("✅ Video uploaded and compressed successfully!")
+if uploaded_video is not None:
+    st.video(uploaded_video)
+    if st.button("Analyze Video"):
+        st.write("🔍 Processing...")
+        # Deepfake video detection logic (Placeholder)
+        st.warning("⚠️ Result: This video contains Deepfake elements.")  # Replace with model
 
-# 👉 Text Input for Fake News Detection
-elif option == "📝 Text":
-    text_input = st.text_area("Enter your text for analysis")
-    if text_input:
-        st.write("🔍 Fake news detection processing...")
-        st.success("✅ Text analysis completed!")
+st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")