Update app.py

app.py

@@ -1,65 +1,27 @@
 import streamlit as st
-import cv2
-import numpy as np
-from ultralytics import YOLO
-from PIL import Image
-import tempfile
 
-# Load YOLO Model
-model = YOLO('yolov8n.pt')
+# Video URL (Replace with your own video link)
+video_url = "https://www.example.com/path-to-your-video.mp4"
 
-st.title("Object & House Size Estimation")
-st.write("Upload an image to detect objects, house size, and wall dimensions.")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+# Embed video using HTML & CSS
+video_html = f"""
+    <style>
+        video {{
+            position: fixed;
+            right: 0;
+            bottom: 0;
+            min-width: 100%;
+            min-height: 100%;
+            z-index: -1;
+        }}
+    </style>
+    <video autoplay loop muted>
+        <source src="{video_url}" type="video/mp4">
+    </video>
+"""
 
-if uploaded_file is not None:
-    with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file:
-        temp_file.write(uploaded_file.read())
-        image_path = temp_file.name
-    
-    image = cv2.imread(image_path)
-    results = model(image)
+# Display the video background
+st.markdown(video_html, unsafe_allow_html=True)
 
-    object_sizes = {}
-    house_size = None
-    wall_size = None
-    
-    for r in results:
-        for box in r.boxes:
-            x1, y1, x2, y2 = map(int, box.xyxy[0])
-            label = model.names[int(box.cls[0])]
-            width = abs(x2 - x1)
-            height = abs(y2 - y1)
-            object_sizes[label] = (width, height)
-            cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2)
-            cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
-            
-            if "house" in label.lower():
-                house_size = (width, height)
-            elif "wall" in label.lower():
-                wall_size = (width, height)
-    
-    st.image(image, caption="Detected Objects", use_column_width=True)
-
-    reference_object = "door"  # Assume a known object for scaling
-    reference_size_inches = 80  # Example: Assume a door is 80 inches tall
-    if reference_object in object_sizes:
-        ref_width_pixels = object_sizes[reference_object][0]
-        scale = reference_size_inches / ref_width_pixels
-        for obj, (w, h) in object_sizes.items():
-            object_sizes[obj] = (round(w * scale, 2), round(h * scale, 2))
-        
-        if house_size:
-            house_size = (round(house_size[0] * scale, 2), round(house_size[1] * scale, 2))
-        if wall_size:
-            wall_size = (round(wall_size[0] * scale, 2), round(wall_size[1] * scale, 2))
-    
-    st.write("Predicted Object Sizes (in inches):")
-    for obj, size in object_sizes.items():
-        st.write(f"{obj}: Width = {size[0]} inches, Height = {size[1]} inches")
-    
-    if house_size:
-        st.write(f"House Size: Width = {house_size[0]} inches, Height = {house_size[1]} inches")
-    if wall_size:
-        st.write(f"Wall Size: Width = {wall_size[0]} inches, Height = {wall_size[1]} inches")
+st.title("Object & House Size Estimation")
+st.write("Upload an image to detect objects, house size, and wall dimensions.")