Fixes

app.py

@@ -0,0 +1,214 @@
+import time
+import sys
+import streamlit as st
+import string
+import os
+from io import StringIO 
+import pdb
+import json
+import torch
+import requests
+import socket
+from streamlit_image_select import image_select
+
+
+
+
+
+use_case = {"1":"Image background removal","2":"Masking foreground for downstream inpainting task"}
+mask_types = {"blur - blurs background":"blur","map - makes the foreground white and rest black ":"map","rgba - makes background white":"rgba","green - makes the background green":"green"}
+
+
+
+
+APP_NAME = "hf/salient_object_detection"
+INFO_URL = "https://www.taskswithcode.com/stats/"
+TMP_DIR="tmp_dir"
+TMP_SEED = 1
+
+
+
+        
+
+def get_views(action):
+    ret_val = 0
+    #return "{:,}".format(ret_val)
+    hostname = socket.gethostname()
+    ip_address = socket.gethostbyname(hostname)
+    if ("view_count" not in st.session_state):
+        try:
+           app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address}
+           res = requests.post(INFO_URL, json = app_info).json()
+           print(res)
+           data = res["count"]
+        except:
+           data = 0
+        ret_val = data
+        st.session_state["view_count"] = data
+    else:
+        ret_val = st.session_state["view_count"]
+        if (action != "init"):
+           app_info = {'name': APP_NAME,"action":action,"host":hostname,"ip":ip_address}
+           res = requests.post(INFO_URL, json = app_info).json()
+    return "{:,}".format(ret_val)
+        
+
+
+
+def construct_model_info_for_display(model_names):
+    options_arr  = []
+    #markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Models evaluated ({len(model_names)})</b><br/></div>"
+    markdown_str = f"<div style=\"font-size:16px; color: #2f2f2f; text-align: left\"><br/><b>Model evaluated </b><br/></div>"
+    markdown_str += f"<div style=\"font-size:2px; color: #2f2f2f; text-align: left\"><br/></div>"
+    for node in model_names:
+        options_arr .append(node["name"])
+        if (node["mark"] == "True"):
+            markdown_str += f"<div style=\"font-size:16px; color: #5f5f5f; text-align: left\">&nbsp;•&nbsp;Model:&nbsp;<a href=\'{node['paper_url']}\' target='_blank'>{node['name']}</a><br/>&nbsp;&nbsp;&nbsp;&nbsp;Code released by:&nbsp;<a href=\'{node['orig_author_url']}\' target='_blank'>{node['orig_author']}</a><br/>&nbsp;&nbsp;&nbsp;&nbsp;Model info:&nbsp;<a href=\'{node['sota_info']['sota_link']}\' target='_blank'>{node['sota_info']['task']}</a></div>"
+            if ("Note" in node):
+                markdown_str += f"<div style=\"font-size:16px; color: #a91212; text-align: left\">&nbsp;&nbsp;&nbsp;&nbsp;{node['Note']}<a href=\'{node['alt_url']}\' target='_blank'>link</a></div>"
+            markdown_str += "<div style=\"font-size:16px; color: #5f5f5f; text-align: left\"><br/></div>"
+        
+    markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><b>Note:</b><br/>•&nbsp;Uploaded files are loaded into non-persistent memory for the duration of the computation. They are not cached</div>"
+    markdown_str += "<div style=\"font-size:12px; color: #9f9f9f; text-align: left\"><br/><a href=\'https://github.com/taskswithcode/salient_object_detection_app.git\' target='_blank'>Github code</a> for this app</div>"
+    return options_arr,markdown_str
+
+
+def init_page():
+    st.set_page_config(page_title='TWC - State-of-the-art model salient object detection (visually dominant objects in an image)', page_icon="logo.jpg", layout='centered', initial_sidebar_state='auto',
+            menu_items={
+             'About': 'This app was created by taskswithcode. http://taskswithcode.com'
+             
+              })
+    col,pad = st.columns([85,15])
+
+    with col:
+        st.image("long_form_logo_with_icon.png")
+
+
+def run_test(config,input_file_name,display_area,uploaded_file,mask_type):
+    global TMP_SEED
+    display_area.text("Processing request...")
+    try:
+        if (uploaded_file is None):
+            file_data = open(input_file_name, "rb")
+            r = requests.post(config["SERVER_ADDRESS"], data={"mask":mask_type}, files={"test":file_data})
+        else:
+            file_data = uploaded_file.read()
+            file_name = f"{TMP_DIR}/{TMP_SEED}_{str(time.time()).replace('.','_')}_{uploaded_file.name}"
+            TMP_SEED += 1
+            with open(file_name,"wb") as fp:
+                fp.write(file_data)
+            file_data = open(file_name, "rb")
+            r = requests.post(config["SERVER_ADDRESS"], data={"mask":mask_type}, files={"test":file_data})
+            os.remove(file_name)
+        print("Servers response:",r.status_code,len(r.content))
+        if (r.status_code == 200):
+            size = "{:,}".format(len(r.content))
+            return {"response":r.content,"size":size}
+        else:
+            return {"error":f"API request failed {r.status_code}"}
+    except Exception as e:
+        st.error("Some error occurred during prediction" + str(e))
+        st.stop()
+        return {"error":f"Exception in performing salient object detection: {str(e)}"}
+    return {} 
+
+
+    
+
+def display_results(results,response_info,mask):
+    main_sent = f"<div style=\"font-size:14px; color: #2f2f2f; text-align: left\">{response_info}<br/><br/></div>"
+    body_sent = []
+    download_data = {}
+    main_sent = main_sent + "\n" + '\n'.join(body_sent)
+    st.markdown(main_sent,unsafe_allow_html=True)
+    st.image(results["response"], caption=f'Output of salient object detection with mask: {mask}')
+    st.session_state["download_ready"]  = results["response"]
+    get_views("submit")
+
+
+def init_session():
+    print("Init session")
+    init_page()
+    st.session_state["model_name"] = "insprynet"
+    st.session_state["download_ready"] = None    
+    st.session_state["model_name"] = "ss_test"
+    st.session_state["file_name"] = "default"
+    st.session_state["mask_type"] = "blur"
+ 
+def app_main(app_mode,example_files,model_name_files,config_file):
+  init_session()
+  with open(example_files) as fp:
+        example_file_names = json.load(fp) 
+  with open(model_name_files) as fp:
+        model_names = json.load(fp)
+  with open(config_file) as fp:
+        config = json.load(fp)
+  curr_use_case = use_case[app_mode].split(".")[0]
+  curr_use_case = use_case[app_mode].split(".")[0]
+  st.markdown("<h5 style='text-align: center;'>State-of-the-art model for salient object detection</h5>", unsafe_allow_html=True)
+  st.markdown(f"<div style='color: #4f4f4f; text-align: left'>Use cases for salient object detection<br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;{use_case['1']}<br/>&nbsp;&nbsp;&nbsp;•&nbsp;&nbsp;{use_case['2']}</div>", unsafe_allow_html=True)
+  st.markdown(f"<div style='color: #9f9f9f; text-align: right'>views:&nbsp;{get_views('init')}</div>", unsafe_allow_html=True)
+
+
+  try:
+      
+      
+      with st.form('twc_form'):
+
+        step1_line = "Upload an image or choose an example image below"
+        uploaded_file = st.file_uploader(step1_line, type=["png","jpg","jpeg"])
+
+        selected_file_name = image_select("Select image", ["twc_samples/sample1.jpg", "twc_samples/sample2.jpg", "twc_samples/sample3.jpg", "twc_samples/sample4.jpg"])
+
+
+        st.write("")
+        mask_type = st.selectbox(label=f'Select type of masking',  
+                    options = list(dict.keys(mask_types)), index=0,  key = "twc_mask_types")
+        mask_type = mask_types[mask_type]
+        st.write("")
+        submit_button = st.form_submit_button('Run')
+        options_arr,markdown_str = construct_model_info_for_display(model_names)
+
+        
+        input_status_area = st.empty()
+        display_area = st.empty()
+        if submit_button:
+            start = time.time()
+            if uploaded_file is not None:
+                st.session_state["file_name"]  = uploaded_file.name
+            else:
+                st.session_state["file_name"]  = selected_file_name
+            st.session_state["mask_type"]  = mask_type
+            display_area.empty()
+            results = run_test(config,st.session_state["file_name"],display_area,uploaded_file,mask_type)
+            with display_area.container():
+                if ("error" in results):
+                    st.error(results["error"])
+                else:
+                    device = 'GPU' if torch.cuda.is_available() else 'CPU'
+                    response_info = f"Computation time on {device}: {time.time() - start:.2f} secs for image size: {results['size']} bytes"
+                    display_results(results,response_info,mask_type)
+                    #st.json(results)
+      st.download_button(
+         label="Download results as png",
+         data= st.session_state["download_ready"] if st.session_state["download_ready"] != None else "",
+         disabled = False if st.session_state["download_ready"] != None else True,
+         file_name= (st.session_state["model_name"] + "_"  + st.session_state["mask_type"] + "_" +  '_'.join(st.session_state["file_name"].split(".")[:-1]) + ".png").replace("/","_"),
+         mime='image/png',
+         key ="download" 
+        )
+      
+      
+
+  except Exception as e:
+    st.error("Some error occurred during loading" + str(e))
+    st.stop()  
+	
+  st.markdown(markdown_str, unsafe_allow_html=True)
+  
+ 
+
+if __name__ == "__main__":
+   app_main("1","sod_app_examples.json","sod_app_models.json","config.json")
+

config.json

@@ -0,0 +1 @@
+{"SERVER_ADDRESS":"https://www.taskswithcode.com/salient_object_detection_api/"}

requirements.txt

@@ -0,0 +1,2 @@
+streamlit
+streamlit-image-select

run.sh

@@ -0,0 +1,2 @@
+streamlit run app.py  --server.port 80 "1" "sod_app_examples.json" "sod_app_models.json"
+

sod_app_examples.json

@@ -0,0 +1,5 @@
+{
+"Machine learning terms (phrases test)": {"name":"tests/small_test.txt"},
+"Customer feedback mixed with noise":{"name":"tests/larger_test.txt"},
+"Movie reviews": {"name":"tests/imdb_sent.txt"}
+}

sod_app_models.json

@@ -0,0 +1,19 @@
+[
+
+            {  "name":"InSPyReNet" ,
+                "model":"InSPyReNet",
+                "fork_url":"https://github.com/taskswithcode/InSPyReNet.git",
+                "orig_author_url":"https://github.com/plemeri/inspyrenet",
+                "orig_author":"Taehun Kim",
+                "sota_info": {   
+                                 "task":"This model was #1 SOTA on 8+ datasets",
+                                 "sota_link":"https://paperswithcode.com/paper/revisiting-image-pyramid-structure-for-high"
+                            },
+                "paper_url":"https://arxiv.org/abs/2209.09475v1",
+                "mark":"True",
+                "Note":"This model can perform SOD on video too. For SOD on videos use the Colab link ",
+                "alt_url":"https://github.com/taskswithcode/InSPyReNet/blob/main/TWCSOD.ipynb",
+                "class":"SODModel","sota_link":"https://arxiv.org/abs/2209.09475v1"}
+
+
+            ]