Update app.py

app.py

@@ -1,14 +1,3 @@
-import os
-import sys
-import subprocess
-
-# ⬇️ Install `segment_anything` if not already installed
-try:
-    from segment_anything import SamPredictor, sam_model_registry
-except ImportError:
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "git+https://github.com/facebookresearch/segment-anything.git"])
-    from segment_anything import SamPredictor, sam_model_registry
-
 import streamlit as st
 import cv2
 import numpy as np
@@ -16,16 +5,17 @@ import pandas as pd
 from PIL import Image
 import torch
 from torchvision.transforms import Compose, Resize, ToTensor, Normalize
+from segment_anything import SamPredictor, sam_model_registry
 
 # Set Streamlit configuration
 st.set_page_config(page_title="Volume Estimator", layout="wide")
-st.title("📦 Volume Estimation using SAM Segmentation + MiDaS Depth")
+st.title("Volume Estimation using SAM Segmentation + MiDaS Depth")
 
 # Load SAM and MiDaS models
 @st.cache_resource
 def load_models():
-    sam_checkpoint = "https://drive.google.com/file/d/1pgv2kKrytyY2_uHSypJh5yQz2BRlfjSS/view?usp=drive_link/sam_vit_b_01ec64.pth"  # Make sure this file is present in the same folder
-    sam = sam_model_registry["vit_b"](checkpoint=sam_checkpoint).to("cuda" if torch.cuda.is_available() else "cpu")
+    sam_checkpoint = "https://drive.google.com/file/d/1pgv2kKrytyY2_uHSypJh5yQz2BRlfjSS/view?usp=drive_link/sam_vit_h_4b8939.pth"
+    sam = sam_model_registry["vit_h"](checkpoint=sam_checkpoint).to("cuda" if torch.cuda.is_available() else "cpu")
     predictor = SamPredictor(sam)
 
     midas = torch.hub.load("intel-isl/MiDaS", "DPT_Large")
@@ -65,7 +55,7 @@ elif source_option == "Use Webcam":
             frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
             stframe.image(frame_rgb, caption="Live Camera Feed", channels="RGB")
 
-            if st.button("📸 Capture Frame"):
+            if st.button("Capture Frame"):
                 image_pil = Image.fromarray(frame_rgb)
                 run_camera = False
                 cap.release()
@@ -127,10 +117,10 @@ if image_pil:
     # Display volume table
     if volume_data:
         df = pd.DataFrame(volume_data)
-        st.markdown("### 📊 Object Dimensions and Volume")
+        st.markdown("### Object Dimensions and Volume")
         st.dataframe(df)
 
         csv = df.to_csv(index=False).encode('utf-8')
-        st.download_button("📂 Download Volume Table as CSV", csv, "object_volumes_with_units.csv", "text/csv")
+        st.download_button("Download Volume Table as CSV", csv, "object_volumes_with_units.csv", "text/csv")
     else:
-        st.warning("🚫 No objects were segmented.")
+        st.warning("No objects were segmented.")