app prompting

app.py

@@ -1,4 +1,76 @@
 import streamlit as st
+import pandas as pd
+import numpy as np
+
+from transformers import pipeline
+
+
+from nltk.tokenize import sent_tokenize
+import nltk
+
+@st.cache()
+def download_punkt():
+    nltk.download('punkt')
+
+download_punkt()
+
+def choose_text_menu(text):
+
+    text = st.text_area('Text to analyze', 'Several demonstrators were injured.')
+
+    return text
+
+
+# Load Models in cache
+@st.cache(allow_output_mutation=True)
+def load_model_prompting():
+    return pipeline("fill-mask", model="distilbert-base-uncased")
+
+
+###### Prompting
+def query_model_prompting(model, text, prompt_with_mask, top_k, targets):
+    sequence = text + prompt_with_mask
+    output_tokens = model(sequence, top_k=top_k, targets=targets)
+    
+    return output_tokens
+
+
+def display_pr_results_as_list(prompt, list_results):
+    prompt_mix_list = st.multiselect(
+        prompt,
+        list_results
+        ,
+        list_results, key='results_mix')
+
+
+### App START
+st.markdown("# Tag text based on the Prompt approach")
+st.markdown("## Author: ...")
+st.markdown("**Tips:** - If the [MASK] of your prompt is at the end of the sentence. Don't forget to put a punctuation sign after or the prompt will outputs punctuations.")
+
+
+model_prompting = load_model_prompting()
+
+prompt_mix = st.text_input('Prompt with a [MASK]:','This event involves [MASK].')
+prompt_mix_list = [prompt_mix]
+
+
+
+top_k = st.number_input('Number of max tokens to output (higher = more computation time)? ',step = 1, min_value=0, max_value=50, value=10)
+
+text = choose_text_menu('')
+
+for prompt in prompt_mix_list:
+    model_load_state = st.text('Tagging Running')
+    prompt = prompt.replace('[MASK]', '{}')
+    output_tokens = query_model_prompting(model_prompting, text, prompt, top_k, targets=None)
+    list_results = []
+    for each in output_tokens:
+        list_results.append(each["token_str"] + ' ' + str(int(each['score']*100)) + '%')
+    display_pr_results_as_list(prompt, list_results)
+    model_load_state.text("Tagging Done!")
+
+
+
+
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)

requirements.txt

@@ -0,0 +1,6 @@
+# see environments.yml
+numpy
+pandas
+transformers[torch]
+nltk
+sentence_transformers