update

app.py

@@ -1,4 +1,3 @@
-#!/usr/bin/env python
 # -*- coding: utf-8 -*-
 """
 -------------------------------------------------
@@ -8,22 +7,55 @@
    @Description:
 -------------------------------------------------
 """
+#from dotenv import load_dotenv
+#load_dotenv()
+
 from pathlib import Path
 from PIL import Image
 import streamlit as st
 
 import config
 from utils import load_model, infer_uploaded_image, infer_uploaded_video, infer_uploaded_webcam
-from download_gd import *
 import os
 
-if not os.path.exists('./crowded_human_yolov8s.pt'):
-    download_file_from_google_drive('1qCXBDy3YuxS9bqfRIc--cRLo1L3DAYq2', './crowded_human_yolov8s.pt')
+base_download_path = "downloaded"
+#------------------------
+if not os.path.exists(base_download_path):
+    os.makedirs(base_download_path)
 
-if os.path.exists('./crowded_human_yolov8s.pt'):
-    file_exist = "./crowded_human_yolov8s.pt"
-else:
-    file_exist = "No file"
+#model_count = int(os.getenv("model_count"))
+model_count = int(st.secrets["model_count"])
+
+model_info = {}
+models_list = []
+for i in range(0, model_count):
+    '''
+    model_name = os.getenv("m{}_name".format(i))
+    gdrive_id = os.getenv("m{}_griv".format(i))
+    model_extname = os.getenv("m{}_type".format(i))
+    model_desc = os.getenv("m{}_desc".format(i))
+    '''
+    model_name = st.secrets["m{}_name".format(i)]
+    gdrive_id = st.secrets["m{}_griv".format(i)]
+    model_extname = st.secrets["m{}_type".format(i)]
+    model_desc = st.secrets["m{}_desc".format(i)]
+    print(i, model_name, gdrive_id, model_extname, model_desc)
+
+    path_model = os.path.join(base_download_path, model_name + model_extname)
+    print('path_model', path_model)
+    model_info.update( {model_desc:path_model} )
+    models_list.append(model_desc)
+
+    if not os.path.exists(path_model):
+        download_link = "https://drive.google.com/uc?export=download&confirm=t&id={}".format(gdrive_id)
+        #subprocess.Popen( 'gdown {}'.format(download_link)
+        #if gdrive_id[:4] == "http":
+        print('wget -O {} --content-disposition "{}"'.format(path_model, download_link))
+        os.system( 'wget -O {} --content-disposition "{}"'.format(path_model, download_link))
+        #else:
+        #    download_file_from_google_drive(gdrive_id, path_model)
+
+print('models_list', models_list)
 
 # setting page layout
 st.set_page_config(
@@ -34,31 +66,34 @@ st.set_page_config(
     )
 
 # main page heading
-st.title(file_exist)
+st.title("Models Demo")
 
 # sidebar
 st.sidebar.header("DL Model Config")
 
 # model options
 task_type = "Detection"
-model_type = "crowded_human"
+
+model_type = st.sidebar.selectbox(
+   "Model types",
+   tuple(models_list))
 
 confidence = float(st.sidebar.slider(
     "Select Model Confidence", 30, 100, 50)) / 100
 
 if model_type:
     #model_path = Path(config.DETECTION_MODEL_DIR, str(model_type))
-    model_path = "./crowded_human_yolov8s.pt"
+    model_path = model_info[model_type]
+
+    try:
+        print('model_path', model_path)
+        model = load_model(model_path)
+    except Exception as e:
+        st.error(f"Unable to load model. Please check the specified path: {model_path}")
+
 else:
     st.error("Please Select Model in Sidebar")
 
-# load pretrained DL model
-try:
-    print('model_path', model_path)
-    model = load_model(model_path)
-except Exception as e:
-    st.error(f"Unable to load model. Please check the specified path: {model_path}")
-
 # image/video options
 st.sidebar.header("Image/Video Config")
 source_selectbox = st.sidebar.selectbox(