update app, v1

app.py

@@ -1,23 +1,120 @@
+import numpy as np
+from transformers import pipeline
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+
 import gradio as gr
-import pandas as pd
-import seaborn as sns
 import matplotlib.pyplot as plt
 
+dates = []
+dates.extend([f"18 {m}" for m in range(1,13)])
+dates.extend([f"19 {m}" for m in range(1,13)])
+dates.extend([f"20 {m}" for m in range(1,13)])
+dates.extend([f"21 {m}" for m in range(1,13)])
+
+months = [x.split(" ")[-1] for x in dates]
+
+model_name = "fvancesco/tmp_date"
+model = AutoModelForMaskedLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model.eval()
+pipe = pipeline('fill-mask', model=model, tokenizer=tokenizer, device=0)
+
+last_mf_dict = None
+
+
+def get_mf_dict(text):
+
+    # predictions
+    texts = []
+    for d in dates:
+        texts.append(f"{d} {text}")
+    tmp_preds = pipe(texts, top_k=50265)
+    preds = {}
+    for i in range(len(tmp_preds)):
+        preds[dates[i]] = tmp_preds[i]
+
+    # get preds summary (only top words)
+    top_n = 5 # top n for each prediction
+    most_freq_tokens = set()
+    for d in dates:
+        tmp = [t['token_str'] for t in preds[d][:top_n]]
+        most_freq_tokens.update(tmp)
+
+    token_prob = {}
+    for d in dates:
+        token_prob[d] = {p['token_str']:p['score'] for p in preds[d]}
+
+    mf_dict = {p:np.zeros(len(dates)) for p in most_freq_tokens}
+
+    c=0
+    for d in dates:
+        for t in most_freq_tokens:
+            mf_dict[t][c] = token_prob[d][t]
+        c+=1
+
+    return mf_dict
 
-def plot_pens(alpha):
-    df_pens = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv')
-    fig = plt.figure()
-    plt.scatter(x=df_pens['bill_length_mm'], y=df_pens['bill_depth_mm'])
+def plot_time(text):
+    mf_dict = get_mf_dict(text)
+    #last_mf_dict = mf_dict # just for debugging, remove in final version
+    
+    #max_tokens = 10
+
+    fig = plt.figure(figsize=(16,9))
+    ax = fig.add_subplot(111)
+    #fig, ax = plt.subplots(figsize=(16,9))
+
+    x = [i for i in range(len(dates))]
+
+    ax.set_xlabel('Month')
+    ax.set_xlim(0)
+    ax.set_xticks(x)
+    ax.set_xticklabels(months)
+    # ax.set_yticks([-1,0,1])
+
+    ax2 = ax.twiny()
+    ax2.set_xlabel('Year')
+    ax2.set_xlim(0)
+    ax2.set_xticks([0,12,24,36,47])
+
+    ax2.set_xticklabels('')
+    ax2.set_xticks([6,18,30,42,47], minor=True)
+    ax2.set_xticklabels(['2018','2019','2020','2021',''], minor=True)
+
+    ax2.grid()
+
+    # plot lines
+    for k in mf_dict.keys():
+        ax.plot(x, mf_dict[k], label = k)
+    # k = list(mf_dict.keys())
+    # for i in range(max_tokens):
+    #     ax.plot(x, mf_dict[k[i]], label = k[i])
+
+    ax.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
+    
     return fig
 
+def add_mask(text):
+    out = ""
+    if len(text) == 0 or text[-1] == " ": 
+        out = text+"<mask>"
+    else: 
+        out = text+" <mask>"
+    return out
+
+with gr.Blocks() as demo:
+    #textbox = gr.Textbox(placeholder="Type here and press enter...")
+    textbox = gr.Textbox(value="Happy <mask>!", max_lines=1)
+
+    with gr.Row():
+        generate_btn = gr.Button("Generate Plot")
+        mask_btn = gr.Button("Add <mask>")
+
+    plot = gr.Plot()
+    
+    #textbox.change(fn=plot_time, inputs=textbox, outputs=plot)
+    #textbox.click(fn=plot_time, inputs=textbox, outputs=plot)
+    generate_btn.click(fn=plot_time, inputs=textbox, outputs=plot)
+    mask_btn.click(fn=add_mask, inputs=textbox, outputs=textbox)
 
-iface = gr.Interface(
-    fn=plot_pens, 
-    layout='vertical',
-    inputs=['checkbox'],
-    outputs=['plot'],
-    title="Scatterplot of Palmer Penguins",
-    description="Let's talk pens.",
-    article="Talk more about Penguins here, shall we?",
-    theme='peach'
-).launch()
+demo.launch(debug=True)

requirements.txt

@@ -1,2 +1 @@
-torch
 transformers