format file and remove share=True

app.py

@@ -31,6 +31,7 @@ for bert_like in MODEL_NAMES:
 
 # %%
 
+
 def clean_tokens(tokens):
     return [token.strip() for token in tokens]
 
@@ -61,8 +62,6 @@ def get_avg_prob_from_pipeline_outputs(mask_filled_text, gendered_token, num_pre
     return round(sum(pronoun_preds) / (EPS + num_preds) * 100, DECIMAL_PLACES)
 
 
-
-
 def get_figure(df, gender, n_fit=1):
     df = df.set_index("x-axis")
     cols = df.columns
@@ -75,16 +74,16 @@ def get_figure(df, gender, n_fit=1):
 
     # find stackoverflow reference
     p, C_p = np.polyfit(xs, ys, n_fit, cov=1)
-    t = np.linspace(min(xs)-1, max(xs)+1,  10*len(xs))
-    TT = np.vstack([t**(n_fit-i) for i in range(n_fit+1)]).T
+    t = np.linspace(min(xs) - 1, max(xs) + 1, 10 * len(xs))
+    TT = np.vstack([t ** (n_fit - i) for i in range(n_fit + 1)]).T
 
     # matrix multiplication calculates the polynomial values
     yi = np.dot(TT, p)
     C_yi = np.dot(TT, np.dot(C_p, TT.T))  # C_y = TT*C_z*TT.T
     sig_yi = np.sqrt(np.diag(C_yi))  # Standard deviations are sqrt of diagonal
 
-    ax.fill_between(t, yi+sig_yi, yi-sig_yi, alpha=.25)
-    ax.plot(t, yi, '-')
+    ax.fill_between(t, yi + sig_yi, yi - sig_yi, alpha=0.25)
+    ax.plot(t, yi, "-")
     ax.plot(df, "ro")
     ax.legend(list(df.columns))
 
@@ -97,7 +96,6 @@ def get_figure(df, gender, n_fit=1):
     return fig
 
 
-
 # %%
 def predict_masked_tokens(
     model_name,
@@ -185,34 +183,33 @@ def predict_masked_tokens(
 
 truck_fn_example = [
     MODEL_NAMES[2],
-    '',
-    ', '.join(['truck', 'pickup']),
-    ', '.join(['car', 'sedan']),
-    ', '.join(['city','neighborhood','farm']),
-    'PLACE',
+    "",
+    ", ".join(["truck", "pickup"]),
+    ", ".join(["car", "sedan"]),
+    ", ".join(["city", "neighborhood", "farm"]),
+    "PLACE",
     "True",
     1,
 ]
+
+
 def truck_1_fn():
-    return truck_fn_example + [
-        'He loaded up his truck and drove to the PLACE.'
-    ]
+    return truck_fn_example + ["He loaded up his truck and drove to the PLACE."]
+
 
 def truck_2_fn():
     return truck_fn_example + [
-        'He loaded up the bed of his truck and drove to the PLACE.'
+        "He loaded up the bed of his truck and drove to the PLACE."
     ]
 
 
 # # %%
 
 
-
 demo = gr.Blocks()
 with demo:
     gr.Markdown("# Spurious Correlation Evaluation for Pre-trained LLMs")
 
-
     gr.Markdown("## Instructions for this Demo")
     gr.Markdown(
         "1) Click on one of the examples below to pre-populate the input fields."
@@ -224,8 +221,8 @@ with demo:
         "3) Repeat steps (1) and (2) with more pre-populated inputs or with your own values in the input fields!"
     )
 
-
-    gr.Markdown("""The pre-populated inputs below are for a demo example of a location-vs-vehicle-type spurious correlation.
+    gr.Markdown(
+        """The pre-populated inputs below are for a demo example of a location-vs-vehicle-type spurious correlation.
         We can see this spurious correlation largely disappears in the well-specified example text.
 
         <p align="center">
@@ -236,18 +233,25 @@ with demo:
         <p align="center">
         <img src="file/well_spec.png" alt="results" width="300"/>
         </p>
-    """)
+    """
+    )
 
     gr.Markdown("## Example inputs")
     gr.Markdown(
         "Click a button below to pre-populate input fields with example values. Then scroll down to Hit Submit to generate predictions."
     )
     with gr.Row():
-        truck_1_gen = gr.Button("Click for non-well-specified(?) vehicle-type example inputs")
-        gr.Markdown("<-- Multiple solutions with low training error. LLM sensitive to spurious(?) correlations.")
+        truck_1_gen = gr.Button(
+            "Click for non-well-specified(?) vehicle-type example inputs"
+        )
+        gr.Markdown(
+            "<-- Multiple solutions with low training error. LLM sensitive to spurious(?) correlations."
+        )
 
         truck_2_gen = gr.Button("Click for well-specified vehicle-type example inputs")
-        gr.Markdown("<-- Fewer solutions with low training error. LLM less sensitive to spurious(?) correlations.")
+        gr.Markdown(
+            "<-- Fewer solutions with low training error. LLM less sensitive to spurious(?) correlations."
+        )
 
     gr.Markdown("## Input fields")
     gr.Markdown(
@@ -343,11 +347,37 @@ with demo:
         )
 
     with gr.Row():
-        truck_1_gen.click(truck_1_fn, inputs=[], outputs=[model_name, own_model_name, group_a_tokens, group_b_tokens,
-                       x_axis, place_holder, to_normalize,  n_fit, input_text])
+        truck_1_gen.click(
+            truck_1_fn,
+            inputs=[],
+            outputs=[
+                model_name,
+                own_model_name,
+                group_a_tokens,
+                group_b_tokens,
+                x_axis,
+                place_holder,
+                to_normalize,
+                n_fit,
+                input_text,
+            ],
+        )
 
-        truck_2_gen.click(truck_2_fn, inputs=[], outputs=[model_name, own_model_name, group_a_tokens, group_b_tokens,
-                       x_axis, place_holder, to_normalize,  n_fit, input_text])
+        truck_2_gen.click(
+            truck_2_fn,
+            inputs=[],
+            outputs=[
+                model_name,
+                own_model_name,
+                group_a_tokens,
+                group_b_tokens,
+                x_axis,
+                place_holder,
+                to_normalize,
+                n_fit,
+                input_text,
+            ],
+        )
 
     btn.click(
         predict_masked_tokens,
@@ -365,8 +395,6 @@ with demo:
         outputs=[sample_text, female_fig, male_fig, df],
     )
 
-demo.launch(debug=True, share=True)
+demo.launch(debug=True)
 
 # %%
-
-