create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import pipeline, TextClassificationPipeline
+pipe = pipeline(model="raminass/scotus-v10", top_k=13, padding=True, truncation=True)
+
+def average_text(text, model):
+  # result = classifier(df_train[(df_train.case_name==case) & (df_train.category=='per_curiam')]['clean_text'].to_list())
+  result = model(text)
+  pred = {}
+  for c in result:
+    for d in c:
+      if d['label'] not in pred:
+        pred[d['label']] = [round(d['score'],2)]
+      else:
+        pred[d['label']].append(round(d['score'],2))
+  sumary = {k:round(sum(v)/len(v),2) for k,v in pred.items()}
+  result = [[{k: round(v, 2) if k=='score' else v for k, v in dct.items()} for dct in lst ]  for lst in result]
+  return dict(sorted(sumary.items(), key=lambda x: x[1],reverse=True)), result
+
+def greet(opinion):
+    result = average_text(chunk_data(remove_citations(opinion))['text'].to_list(),pipe)
+    # print(f"average prediction:")
+    # display(result[0])
+    # print(f"paragraph prediction:")
+    # display(result[1])
+    return result[0]
+
+with gr.Blocks() as demo:
+    opinion = gr.Textbox(label="Opinion")
+    output = gr.Textbox(label="Result")
+    greet_btn = gr.Button("Predict")
+    greet_btn.click(fn=greet, inputs=opinion, outputs=output, api_name="SCOTUS")
+
+if __name__ == "__main__":
+    demo.launch()