Update app.py

app.py

@@ -44,54 +44,61 @@
 # st.markdown("🔹 **Developed for Fake News & Deepfake Detection Hackathon**")
 
 
-import gradio as gr
+import streamlit as st
 import cv2
 import numpy as np
-
-# ✅ Image Size Limit (MB) 
-MAX_IMAGE_SIZE_MB = 5  # 5MB se zyada allow nahi karega
-
-# ✅ Video Size Limit (MB) 
-MAX_VIDEO_SIZE_MB = 20  # 20MB se zyada allow nahi karega
-
-# ✅ Image Processing Function
-def process_image(image):
-    if image is None:
-        return "❌ No image uploaded!"
+import tempfile
+import os
+from PIL import Image
+
+def compress_image(image, quality=20):
+    img = Image.open(image)
+    img = img.convert("RGB")
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
+    img.save(temp_file.name, "JPEG", quality=quality)
+    return temp_file.name
+
+def compress_video(video):
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
+    cap = cv2.VideoCapture(video)
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    frame_width = int(cap.get(3) // 2)
+    frame_height = int(cap.get(4) // 2)
+    out = cv2.VideoWriter(temp_file.name, fourcc, 20.0, (frame_width, frame_height))
     
-    # ✅ Image Resize (Optional: 512x512)
-    image = cv2.resize(image, (512, 512))  
-    return image
-
-# ✅ Video Processing Function
-def process_video(video_path):
-    if video_path is None:
-        return "❌ No video uploaded!"
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+        frame = cv2.resize(frame, (frame_width, frame_height))
+        out.write(frame)
     
-    # ✅ Video Size Check
-    import os
-    file_size_mb = os.path.getsize(video_path) / (1024 * 1024)
-    if file_size_mb > MAX_VIDEO_SIZE_MB:
-        return f"❌ Video is too large! (Size: {file_size_mb:.2f}MB) - Limit: {MAX_VIDEO_SIZE_MB}MB"
-    
-    return f"✅ Video uploaded successfully! (Size: {file_size_mb:.2f}MB)"
-
-# ✅ Gradio Interface
-with gr.Blocks() as app:
-    gr.Markdown("## 🕵️‍♂️ Fake News & Deepfake Detection Tool")
-
-    with gr.Row():
-        img_input = gr.Image(type="numpy", label="🖼 Upload Image (Max: 5MB)")
-        img_output = gr.Image(label="📌 Processed Image")
-        img_button = gr.Button("🔍 Detect Image")
-
-    img_button.click(process_image, inputs=img_input, outputs=img_output)
-
-    with gr.Row():
-        video_input = gr.File(label="🎥 Upload Video (Max: 20MB)", file_types=[".mp4"])
-        video_output = gr.Textbox(label="📌 Video Status")
-        video_button = gr.Button("🔍 Detect Video")
-
-    video_button.click(process_video, inputs=video_input, outputs=video_output)
-
-app.launch()
+    cap.release()
+    out.release()
+    return temp_file.name
+
+st.title("🕵️‍♂️ Fake News & Deepfake Detection Tool")
+
+st.sidebar.header("Upload your file")
+option = st.sidebar.radio("Select file type", ["Image", "Video", "Text"])
+
+if option == "Image":
+    uploaded_file = st.sidebar.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file is not None:
+        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!")
+
+elif option == "Video":
+    uploaded_file = st.sidebar.file_uploader("Upload a video", type=["mp4", "avi", "mov"])
+    if uploaded_file is not None:
+        compressed_path = compress_video(uploaded_file)
+        st.video(compressed_path)
+        st.success("✅ Video uploaded and compressed successfully!")
+
+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!")