Upload folder using huggingface_hub

app.py

@@ -8,7 +8,8 @@ all = load_dataset("raminass/full_opinions_1994_2020")
 df = pd.DataFrame(all["train"])
 choices = []
 for index, row in df[df.category == "per_curiam"].iterrows():
-    choices.append((f"""{row["case_name"]}""", [row["text"], row["year_filed"]]))
+    if len(row["text"]) > 1000:
+        choices.append((f"""{row["case_name"]}""", [row["text"], row["year_filed"]]))
 
 max_textboxes = 100
 
@@ -37,16 +38,18 @@ def greet(opinion, year):
 
 
 def set_input(drop):
-    return drop[0], drop[1]
+    return drop[0], drop[1], gr.Slider(visible=False)
 
 
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             opinion = gr.Textbox(label="Opinion")
-            year = gr.Slider(1994, 2020, label="Year")
+            year = gr.Slider(1994, 2020, step=1, label="Year")
             drop = gr.Dropdown(choices=sorted(choices))
-            greet_btn = gr.Button("Predict")
+            with gr.Row():
+                clear_btn = gr.Button("Clear")
+                greet_btn = gr.Button("Predict")
         op_level = gr.outputs.Label(num_top_classes=13, label="Overall")
 
     textboxes = []
@@ -59,7 +62,7 @@ with gr.Blocks() as demo:
         textboxes.append(t)
         textboxes.append(par_level)
 
-    drop.select(set_input, inputs=drop, outputs=[opinion, year])
+    drop.select(set_input, inputs=drop, outputs=[opinion, year, year])
 
     greet_btn.click(
         fn=greet,
@@ -67,6 +70,12 @@ with gr.Blocks() as demo:
         outputs=[op_level] + textboxes,
     )
 
+    clear_btn.click(
+        fn=lambda: [None, 1994, gr.Slider(visible=True), None, None]
+        + [gr.Textbox(visible=False), gr.Label(visible=False)] * max_textboxes,
+        outputs=[opinion, year, year, drop, op_level] + textboxes,
+    )
+
 
 if __name__ == "__main__":
     demo.launch()