update

app.py

@@ -3,6 +3,9 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import huggingface_hub as hfh
+import requests
+
+os.makedirs("labels", exist_ok=True)
 
 voters = [
     "osman",
@@ -37,15 +40,31 @@ def get_one_from_queue(voter: str):
     # aifred-smart-life-coach/labels labels dataset
     # labels dataset multiple csv files named as [voter name].csv
     # each csv file has the image image path vote date, votes
-    files = api.list_repo_tree(repo_id="aifred-smart-life-coach/labels", repo_type="dataset", recursive=True,)
+    url = f"https://huggingface.co/datasets/aifred-smart-life-coach/labels/raw/main/{voter}.csv"
+
+    # fetch file and save it to the labels folder
+    file_path = f"labels/{voter}.csv"
+    req = requests.get(url)
+    with open(file_path, "wb") as file:
+        file.write(req.content)
+
+    df = pd.read_csv(file_path)
+    print(df)
+    num_past_votes = df.shape[0]
+    print("num_past_votes", num_past_votes)
+
+    list_of_images = get_list_of_images()
+    print("list_of_images", len(list_of_images))
+
+    # get the list of images that are not present in the csv file
+    images_not_voted = list(set(list_of_images) - set(df["image_path"].tolist()))
+    print("images_not_voted", len(images_not_voted))
+
 
-    for file in files:
-        if file.path == f"{voter}.csv":
-            df = pd.read_csv(api.download_file(file.path))
-            print(df)
+    return {"image": images_not_voted[0]} if images_not_voted else False
 
-    return None
 
+print(get_one_from_queue("osman"))
 
 if submitted:
     if not password == os.environ.get("app_password"):
@@ -60,7 +79,13 @@ if submitted:
         if not queue:
             st.write("You have voted for all the images")
             st.stop()
-        st.image(queue["image"])
+        # https://huggingface.co/datasets/aifred-smart-life-coach/capstone-images/resolve/main/kaggle-human-segmentation-dataset/Women%20I/img/woman_image_200.jpg
+        st.image(f"https://huggingface.co/datasets/aifred-smart-life-coach/capstone-images/resolve/main/{queue['image']}", width=300)
+        gender = st.select([
+            "Male",
+            "Female",
+            "Non-defining",
+        ])
         healthiness = st.slider("How healthy is this picture?", 0, 100, 50)
         fat_level = st.slider("How fat is this picture?", 0, 100, 50)
         muscle_level = st.slider("How muscular is this picture?", 0, 100, 50)