test

app.py

@@ -2,18 +2,76 @@ import gradio as gr
 
 import pandas as pd
 import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.decomposition import LatentDirichletAllocation
+from sklearn.feature_extraction.text import CountVectorizer
 
-def greet(name):
-    pd.read_csv('./data/results.csv', index_col=0)
-    
-    return "Hello " + name + "!!"
+def concat_comments(sup_comment: list[str], comment: list[str]) -> list[str]:
+    format_s = "{s}\n{c}"
+    return [
+        format_s.format(s=s, c=c) for s, c in zip(sup_comment, comment)
+    ]
 
 
 
+def main(button, chose_context):
+    df = pd.read_csv('./data/results.csv', index_col=0)
+    print(chose_context)
 
+    data = concat_comments(df.sup_comment, df.comment)
 
 
+    subreddits = df.subreddit.value_counts().index[:22]
 
+    weight_counts = {
+        t: [
+            df[df.Topic_key_word == t].subreddit.value_counts()[subreddit] / df.subreddit.value_counts()[subreddit] for subreddit in subreddits
+        ] for t in topics
+    }
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+    irony_percs = {
+        t: [
+            len(
+                df[df.subreddit == subreddit][(df[df.subreddit == subreddit].Topic_key_word == t) & (df[df.subreddit == subreddit].label == 1)]
+            ) / 
+            len(
+                df[df.subreddit == subreddit]
+            ) for subreddit in subreddits
+        ] for t in topics
+    }
+    width = 0.9
+
+    fig, ax = plt.subplots(figsize = (10, 7))
+    plt.axhline(0.5, color = 'red', ls=":", alpha = .3)
+
+    bottom = np.zeros(len(subreddits))
+
+    for k, v in weight_counts.items():
+        p = ax.bar(subreddits, v, width, label=k, bottom=bottom)
+        ax.bar(subreddits, irony_percs[k], width - 0.01, bottom=bottom, color = 'black', edgecolor = 'white', alpha = .2, hatch = '\\')
+        bottom += v
+
+    ax.set_title("Perc of topics for each subreddit")
+    ax.legend(loc="upper right")
+    plt.xticks(rotation=70)
+
+    plt.show()
+
+
+with gr.Blocks() as demo:
+    button = gr.Radio(
+        label="Plot type",
+        choices=['scatter_plot', 'heatmap', 'us_map', 'interactive_barplot', "radial", "multiline"], value='scatter_plot'
+    )
+    chose_context = gr.Radio(
+        label="Context LDA",
+        choices=['comment', 'sup comment', 'sup comment + comment'], value='scatter_plot'
+    )
+    plot = gr.Plot(label="Plot")
+    button.change(main, inputs=[button, chose_context], outputs=[plot])
+    demo.load(main, inputs=[button], outputs=[plot])
+
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+nltk
+spacy
+gensim
+sklearn