WIP

app.py

@@ -72,25 +72,39 @@ class Interactive:
 
 interactive = Interactive()
 
-def predict(statement, model):
+def predict(statement, do_save=True):
     result = interactive.run(statement)
-    with open(DATA_PATH, 'a') as f:
-        row = {
-            'timestamp': datetime.datetime.now().strftime('%Y%m%d-%H%M%S'),
-            'statement': statement,
-            'logit': result['logit'],
-            'logit_calibrated': result['logit_calibrated'],
-            'score': result['score'],
-            'score_calibrated': result['score_calibrated'],
-        }
-        json.dump(row, f, ensure_ascii=False)
-        f.write('\n')
-    commit_url = repo.push_to_hub()
-    print(commit_url)
-    return {
+    output = {
         'True': result['score_calibrated'],
         'False': 1 - result['score_calibrated'],
     }
+    output_raw = {
+        'timestamp': datetime.datetime.now().strftime('%Y%m%d-%H%M%S'),
+        'statement': statement,
+    }
+    output_raw.update(result)
+    if do_save:
+        with open(DATA_PATH, 'a') as f:
+            json.dump(output_raw, f, ensure_ascii=False)
+            f.write('\n')
+        commit_url = repo.push_to_hub()
+        print('Logged statement to dataset:')
+        print('Commit URL:', commit_url)
+        print(output_raw)
+        print()
+    return output, output_raw
+
+def record_feedback(output_raw, feedback, do_save=True):
+    if do_save:
+        output_raw.update({ 'feedback': feedback })
+        with open(DATA_PATH, 'a') as f:
+            json.dump(output_raw, f, ensure_ascii=False)
+            f.write('\n')
+        commit_url = repo.push_to_hub()
+        print('Logged feedback to dataset:')
+        print('Commit URL:', commit_url)
+        print(output_raw)
+        print()
 
 examples = [
     # openbookqa
@@ -155,17 +169,55 @@ examples = [
     # 'If A sits next to B and B sits next to C, then A might not sit next to C.',
 ]
 
-input_statement = gr.Dropdown(choices=examples, label='Statement:')
-input_model = gr.Textbox(label='Commonsense statement verification model:', value=MODEL_NAME, interactive=False)
-output = gr.outputs.Label(num_top_classes=2)
+# input_statement = gr.Dropdown(choices=examples, label='Statement:')
+# input_model = gr.Textbox(label='Commonsense statement verification model:', value=MODEL_NAME, interactive=False)
+# output = gr.outputs.Label(num_top_classes=2)
+
+# description = '''This is a demo for Vera, a commonsense statement verification model. Under development.
+# ⚠️ Data Collection: by default, we are collecting the inputs entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app!'''
+
+# gr.Interface(
+#     fn=predict,
+#     inputs=[input_statement, input_model],
+#     outputs=output,
+#     title="Vera",
+#     description=description,
+# ).launch()
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown(
+            '''# Vera
 
-description = '''This is a demo for Vera, a commonsense statement verification model. Under development.
-⚠️ Data Collection: by default, we are collecting the inputs entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app!'''
+            This is a demo for Vera, a commonsense statement verification model. Under development.
+            ⚠️ Data Collection: by default, we are collecting the inputs entered in this app to further improve and evaluate the model. Do not share any personal or sensitive information while using the app!
+            '''
+        )
+        with gr.Row():
+            with gr.Column(scale=1):
+                do_save = gr.Checkbox(
+                        value=True,
+                        label="Store data",
+                        info="You agree to the storage of your prompt and generated text for research and development purposes:")
+                statement = gr.Textbox(placeholder="Enter a commonsense statement here", label="statement", elem_id="q-input")
+                submit = gr.Button(label='Submit', variant='primary')
+            with gr.Column(scale=1):
+                output = gr.outputs.Label(num_top_classes=2)
+                output_raw = gr.outputs.JSON(visible=True)
+                with gr.Row():
+                    feedback_agree = gr.Button(label='👍 Agree', variant='primary')
+                    feedback_disagree = gr.Button(label='👎 Disagree', variant='primary')
+        with gr.Row():
+            gr.Examples(
+                examples=examples,
+                inputs=[statement],
+                cache_examples=False,
+                fn=predict,
+                outputs=[output, output_raw],
+            )
+    submit.click(predict, inputs=[statement, do_save], outputs=[output])
+    statement.submit(predict, inputs=[statement], outputs=[output])
+    feedback_agree.click(record_feedback, inputs=[output_raw, 'agree', do_save])
+    feedback_disagree.click(record_feedback, inputs=[output_raw, 'disagree', do_save])
 
-gr.Interface(
-    fn=predict,
-    inputs=[input_statement, input_model],
-    outputs=output,
-    title="Vera",
-    description=description,
-).launch()
+demo.queue(concurrency_count=16).launch(debug=True)