Add UniDepth package locally and update requirements

app.py

@@ -2,14 +2,21 @@ import streamlit as st
 import cv2
 import torch
 import numpy as np
-from unidepth.models import UniDepthV2
 from ultralytics import YOLO
 from streamlit_webrtc import webrtc_streamer, VideoTransformerBase
-from UniDepth.unidepth.models import UniDepthV2
 
+# Import local UniDepth
+from unidepth.models import UniDepthV2
+
+# -----------------------------
+# Streamlit setup
+# -----------------------------
 st.set_page_config(page_title="YOLO + UniDepth Streaming", layout="wide")
-st.title("📡 YOLO + UniDepth V2 — Real 3D Distance (Cloud Edition)")
+st.title("📡 YOLO + UniDepth V2 — Real 3D Distance (CPU Edition)")
 
+# -----------------------------
+# Load models (cached)
+# -----------------------------
 @st.cache_resource
 def load_depth_model():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -24,6 +31,9 @@ def load_yolo():
 depth_model, device = load_depth_model()
 yolo_model = load_yolo()
 
+# -----------------------------
+# Helper functions
+# -----------------------------
 def get_depth_points(rgb):
     img_t = torch.from_numpy(rgb).float().permute(2, 0, 1).unsqueeze(0).to(device)/255.0
     with torch.no_grad():
@@ -37,7 +47,9 @@ def process_frame(frame_bgr):
     results = yolo_model(frame_bgr, verbose=False)[0]
     for box in results.boxes:
         conf = box.conf.item()
-        if conf < 0.5: continue
+        if conf < 0.5: 
+            continue
+
         cls_id = int(box.cls.item())
         label = yolo_model.names[cls_id]
 
@@ -48,15 +60,22 @@ def process_frame(frame_bgr):
         distance = np.sqrt(X**2 + Y**2 + Z**2)
 
         cv2.rectangle(frame_bgr, (x1,y1),(x2,y2),(0,255,0),2)
-        cv2.putText(frame_bgr,f"{label} {distance:.2f}m",(x1,y1-10),cv2.FONT_HERSHEY_SIMPLEX,0.7,(0,255,255),2)
+        cv2.putText(frame_bgr, f"{label} {distance:.2f}m", (x1,y1-10),
+                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0,255,255), 2)
 
     return frame_bgr
 
+# -----------------------------
+# WebRTC Video Processor
+# -----------------------------
 class YOLODepthProcessor(VideoTransformerBase):
     def transform(self, frame):
         img = frame.to_ndarray(format="bgr24")
         return process_frame(img)
 
+# -----------------------------
+# WebRTC Streamlit Widget
+# -----------------------------
 st.warning("This app runs in the cloud — your webcam stays private on your device.")
 
 webrtc_streamer(
@@ -75,4 +94,3 @@ webrtc_streamer(
         ]
     }
 )
-