Update app.py

app.py

@@ -168,11 +168,13 @@ def create_embeddings(texts_to_embedd):
 
 
 
-def predict(text_input, progress=gr.Progress()):
+def predict(text_input, sample_size_slider, reduce_sample_checkbox, progress=gr.Progress()):
 
     progress(0.1, desc="Getting queried data...")
     # get data.
     records_df = query_records(text_input)
+    if reduce_sample_checkbox:
+        records_df = records_df.sample(sample_size_slider)
     print(records_df)
     
     
@@ -187,7 +189,7 @@ def predict(text_input, progress=gr.Progress()):
     records_df[['x','y']] = umap_embeddings
 
     basedata_df['color'] = '#ced4d211'
-    records_df['color'] = '#a81a26'
+    records_df['color'] = '#f98e31'
 
     progress(0.6, desc="Set up data...")
 
@@ -246,17 +248,17 @@ def predict(text_input, progress=gr.Progress()):
     plot = datamapplot.create_interactive_plot(
         stacked_df[['x','y']].values,
         np.array(stacked_df['cluster_1_labels']),np.array(stacked_df['cluster_2_labels']),np.array(stacked_df['cluster_3_labels']),
-        hover_text=[str(ix) + ', ' + str(row['parsed_publication']) + str(row['title']) for ix, row in stacked_df.iterrows()],
+        hover_text=[str(row['title']) for ix, row in stacked_df.iterrows()],
         marker_color_array=stacked_df['color'],
 
         use_medoids=True,
-        width=1100,
-        height=900,
+        width=1000,
+        height=1000,
     #    title='The Science of <span style="color:#ab0b00;"> Consciousness </span>',
     #    sub_title=f'<div style="margin-top:20px;"> Large sample, n={len(dataset_df_filtered)}, embeddings with specter 2 & UMAP, labels: Claude 3.5 Sonnet </div>',
         point_radius_min_pixels=1,
         text_outline_width=5,
-        point_hover_color='#ab0b00',
+        point_hover_color='#5e2784',
         point_radius_max_pixels=7,
         color_label_text=False,
         font_family="Roboto Condensed",
@@ -266,7 +268,7 @@ def predict(text_input, progress=gr.Progress()):
         extra_point_data=extra_data,
         on_click="window.open(`{doi}`)",
         custom_css=custom_css,
-        initial_zoom_fraction=.5,
+        initial_zoom_fraction=.8,
         enable_search=True)
 
 
@@ -291,24 +293,42 @@ def predict(text_input, progress=gr.Progress()):
 
 
 
-with gr.Blocks() as block:
-    gr.Markdown("""
-## Gradio + FastAPI + Static Server
-This is a demo of how to use Gradio with FastAPI and a static server.
-The Gradio app generates dynamic HTML files and stores them in a static directory. FastAPI serves the static files.
-""")
-    with gr.Row():
-        with gr.Column():
-            text_input = gr.Textbox(label="Name")
-            markdown = gr.Markdown(label="Output Box")
-            new_btn = gr.Button("New")
-        with gr.Column():
-            html = gr.HTML(label="HTML preview", show_label=True)
+# with gr.Blocks() as block:
+#     gr.Markdown("""
+# ## Mapping OpenAlex-Queries
+
+# This is a tool to further interdisciplinary research – you are a neuroscientist who has used ..., What have the ... been doing with them. 
+# Your a philosopher of science who wonders where the concept of a fitnesslandscape has appeared...
+# """)
+#     with gr.Row():
+#         with gr.Column():
+#             text_input = gr.Textbox(label="Name")
+#             markdown = gr.Markdown(label="Output Box")
+#             new_btn = gr.Button("New")
+#         with gr.Column():
+#             html = gr.HTML(label="HTML preview", show_label=True)
+
+#     new_btn.click(fn=predict, inputs=[text_input], outputs=[markdown, html])
 
-    new_btn.click(fn=predict, inputs=[text_input], outputs=[markdown, html])
 
 
+with gr.Blocks() as block:
+    gr.Markdown("""
+    ## Mapping OpenAlex-Queries
 
+    This is a tool to further interdisciplinary research – you are a neuroscientist who has used ..., What have the ... been doing with them. 
+    You're a philosopher of science who wonders where the concept of a fitness landscape has appeared...
+    """)
+    
+    with gr.Column():
+        text_input = gr.Textbox(label="OpenAlex Fulltext-Search")
+        sample_size_slider = gr.Slider(label="Sample Size", minimum=10, maximum=20000, step=10, value=1000)
+        reduce_sample_checkbox = gr.Checkbox(label="Reduce Sample Size", value=True)
+        new_btn = gr.Button("Run Query")
+        markdown = gr.Markdown(label="")
+        html = gr.HTML(label="HTML preview", show_label=True)
+    
+    new_btn.click(fn=predict, inputs=[text_input, sample_size_slider, reduce_sample_checkbox], outputs=[markdown, html])