added app file and reqs

app.py

@@ -0,0 +1,93 @@
+import streamlit as st
+from datasets import load_dataset
+
+st.set_page_config(page_title="High-Level dataset")
+
+FIELDS = ["scene", "action", "rationale", "object"]
+QS = {
+    "scene": "Where is the picture taken?",
+    "action": "What is the subject doing?",
+    "rationale": "Why is the subject doing it?"
+}
+SPLITS = ["test", "train"]
+
+@st.cache
+def load_data(split):
+
+    #with st.spinner('Loading the data...'):
+    dataset = load_dataset("michelecafagna26/hl")
+
+    coco2id = {int(dataset[split][i]['file_name'].replace("COCO_train2014_", "").replace(".jpg", "")): i for i in
+               range(len(dataset[split]))}
+
+    return dataset, coco2id
+
+
+def write_obj(dataset, img_id, options, split, list_type="num", show_questions=False,
+              show_conf=False):
+    st.image(dataset[split][img_id]['image'])
+
+    # col1, col2 = st.columns(2)
+    #
+    # col1.metric(label="Diversity score",
+    #             value=round(self_bleu[f"COCO_train2014_{img_id}.jpg"], 2),
+    #             delta=round(self_bleu[f"COCO_train2014_{img_id}.jpg"] - AVG_DIVERSITY, 2),
+    #             help="Normalized complementary 3-way Self-BLEU score. The delta is the difference with the average")
+    #
+    # col2.metric(label="Purity score",
+    #             value=round(bleurt[f"COCO_train2014_{img_id}.jpg"], 2),
+    #             delta=round(bleurt[f"COCO_train2014_{img_id}.jpg"] - AVG_PURITY, 2),
+    #             help="Normalized 3-way Bleurt score. The delta is the difference with the average")
+    #
+    for field in options:
+
+        st.markdown(f"## {field.capitalize()}")
+
+        if show_questions and field != "object":
+            st.markdown(f" Question: _{QS[field]}_")
+
+        for n, annotation in enumerate(dataset[split][img_id][field]):
+
+            col1, col2 = st.columns(2)
+
+            if list_type == "num":
+                col1.markdown(f"{n + 1}. {annotation}")
+            else:
+                col1.markdown(f"{list_type} {annotation}")
+
+            if show_conf and field != "object":
+                col2.metric(label="confidence score",
+                            value=dataset[split][img_id]['confidence'][field][n])
+
+
+def main():
+
+    st.title('High-Level Dataset')
+
+    show_questions = st.sidebar.checkbox('Questions')
+    show_conf = st.sidebar.checkbox('Confidence scores')
+    options = st.sidebar.multiselect(
+        'Choose the annotations',
+        FIELDS,
+        default=FIELDS)
+
+    split = st.sidebar.selectbox(
+        'Split',
+        SPLITS)
+
+
+    dataset, coco2id = load_data(split)
+
+    # sidebar
+    choosen_image = st.selectbox(
+        'Select an image',
+        list(coco2id.keys()),
+        help="write a key like: 532"
+    )
+
+    write_obj(dataset, coco2id[choosen_image], options=options, split=split,
+              list_type="num", show_questions=show_questions, show_conf=show_conf)
+
+
+if __name__=="__main__":
+    main()

requirements.txt

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+datasets==2.9.0
+streamlit==1.17.0