table functionality

app.py

@@ -5,22 +5,126 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import os
 import torch
 import numpy as np
-os.environ['KMP_DUPLICATE_LIB_OK'] = "True"
+import pandas as pd
+
 
+os.environ['KMP_DUPLICATE_LIB_OK'] = "True"
 
 
 st.title("Sentiment Analysis App")
-
-labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
+if 'logs' not in st.session_state:
+    st.session_state.logs = dict()
+if 'labels' not in st.session_state:
+    st.session_state.labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
+if 'id2label' not in st.session_state:
+    st.session_state.id2label = {idx: label for idx, label in enumerate(st.session_state.labels)}
+if 'filled' not in st.session_state:
+    st.session_state.filled = False
 
 form = st.form(key='Sentiment Analysis')
-box = form.selectbox('Select Pre-trained Model:', ['bertweet-base-sentiment-analysis',
-                                                   'distilbert-base-uncased-finetuned-sst-2-english',
-                                                   'twitter-roberta-base-sentiment',
-                                                   'Modified Bert Toxicity Classification'
-                                                   ], key=1)
+st.session_state.options = ['bertweet-base-sentiment-analysis',
+           'distilbert-base-uncased-finetuned-sst-2-english',
+           'twitter-roberta-base-sentiment',
+           # 'Modified Bert Toxicity Classification'
+           ]
+box = form.selectbox('Select Pre-trained Model:', st.session_state.options, key=1)
 tweet = form.text_input(label='Enter text to analyze:', value="\"We've seen in the last few months, unprecedented amounts of Voter Fraud.\" @SenTedCruz True!")
 submit = form.form_submit_button(label='Submit')
+if 'df' not in st.session_state:
+    st.session_state.df = pd.read_csv("test.csv")
+
+if not st.session_state.filled:
+    for s in st.session_state.options:
+        st.session_state.logs[s] = []
+if not st.session_state.filled:
+    st.session_state.filled = True
+    for x in range(10):
+        print(x)
+        text = st.session_state.df["comment_text"].iloc[x][:128]
+        for s in st.session_state.options:
+            if s == 'bertweet-base-sentiment-analysis':
+                pline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
+            elif s == 'twitter-roberta-base-sentiment':
+                pline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+            elif s == 'distilbert-base-uncased-finetuned-sst-2-english':
+                pline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+            else:
+                model = AutoModelForSequenceClassification.from_pretrained('./model')
+                model.eval()
+                tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
+                encoding = tokenizer(tweet, return_tensors="pt")
+                encoding = {k: v.to(model.device) for k,v in encoding.items()}
+                predictions = model(**encoding)
+                logits = predictions.logits
+                sigmoid = torch.nn.Sigmoid()
+                probs = sigmoid(logits.squeeze().cpu())
+                predictions = np.zeros(probs.shape)
+                predictions[np.where(probs >= 0.5)] = 1
+                predicted_labels = [st.session_state.id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
+            log = []
+            if pline:
+                predictions = pline(text)
+                log = [0] * 4
+                log[1] = text
+                for p in predictions:
+                    if s == 'bertweet-base-sentiment-analysis':
+                        if p['label'] == "POS":
+                            log[0] = 0
+                            log[2] = "POSITIVE"
+                            log[3] = f"{ round(p['score'] * 100, 1)}%"
+                        elif p['label'] == "NEU":
+                            log[0] = 2
+                            log[2] = f"{ p['label'] }"
+                            log[3] = f"{round(p['score'] * 100, 1)}%"
+                        else:
+                            log[2] = "NEG"
+                            log[0] = 1
+                            log[3] = f"{round(p['score'] * 100, 1)}%"
+                    elif s == 'distilbert-base-uncased-finetuned-sst-2-english':
+                        if p['label'] == "POSITIVE":
+                            log[0] = 0
+                            log[2] = "POSITIVE"
+                            log[3] = (f"{round(p['score'] * 100, 1)}%")
+                        else:
+                            log[2] = ("NEGATIVE")
+                            log[0] = 1
+                            log[3] = (f"{round(p['score'] * 100, 1)}%")
+                    elif s == 'twitter-roberta-base-sentiment':
+                        if p['label'] == "LABEL_2":
+                            log[0] = 0
+                            log[2] = ("POSITIVE")
+                            log[3] = (f"{round(p['score'] * 100, 1)}%")
+                        elif p['label'] == "LABEL_0":
+                            log[0] = 1
+                            log[2] = ("NEGATIVE")
+                            log[3] = f"{round(p['score'] * 100, 1)}%"
+                        else:
+                            log[0] = 2
+                            log[2] = "NEUTRAL"
+                            log[3] = f"{round(p['score'] * 100, 1)}%"
+            else:
+                log = [0] * 6
+                log[1] = text
+                if max(predictions) == 0:
+                    log[0] = 0
+                    log[2] = ("NO TOXICITY")
+                    log[3] = (f"{100 - round(probs[0] * 100, 1)}%")
+                    log[4] = ("N/A")
+                    log[5] = ("N/A")
+                else:
+                    log[0] = 1
+                    _max = 0
+                    _max2 = 2
+                    for i in range(1, len(predictions)):
+                        if probs[i] > probs[_max]:
+                            _max = i
+                        if i > 2 and probs[i] > probs[_max2]:
+                            _max2 = i
+                    log[2] = (st.session_state.labels[_max])
+                    log[3] = (f"{round(probs[_max] * 100, 1)}%")
+                    log[4] = (st.session_state.labels[_max2])
+                    log[5] = (f"{round(probs[_max2] * 100, 1)}%")
+            st.session_state.logs[s].append(log)
 
 if submit and tweet:
     with st.spinner('Analyzing...'):
@@ -32,11 +136,11 @@ if submit and tweet:
         else:
             col1, col2, col3, col4, col5 = st.columns(5)
         if box == 'bertweet-base-sentiment-analysis':
-            pipeline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
+            pline = pipeline(task="sentiment-analysis", model="finiteautomata/bertweet-base-sentiment-analysis")
         elif box == 'twitter-roberta-base-sentiment':
-            pipeline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+            pline = pipeline(task="sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
         elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
-            pipeline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+            pline = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
 
 
             # <--- Unecessary Testing --->
@@ -53,8 +157,8 @@ if submit and tweet:
             predictions = np.zeros(probs.shape)
             predictions[np.where(probs >= 0.5)] = 1
             # turn predicted id's into actual label names
-            id2label = {idx: label for idx, label in enumerate(labels)}
-            predicted_labels = [id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
+            st.session_state.id2label = {idx: label for idx, label in enumerate(st.session_state.labels)}
+            predicted_labels = [st.session_state.id2label[idx] for idx, label in enumerate(predictions) if label == 1.0]
             print(predicted_labels)
             print(predictions[0])
         else:
@@ -64,60 +168,133 @@ if submit and tweet:
             encoding = {k: v.to(model.device) for k,v in encoding.items()}
             predictions = model(**encoding)
             print(predictions)
-            col4
-        if pipeline:
-            predictions = pipeline(tweet)
+        if pline:
+            predictions = pline(tweet)
             col2.header("Judgement")
         else:
-            col2.header("Toxic?")
+            col2.header("")
             col4.header("Toxicity Type")
             col5.header("Probability")
-        print(predictions)
 
         col1.header("Tweet")
         col3.header("Probability")
 
-        col1.subheader(tweet)
-        for p in predictions:
-            if box == 'bertweet-base-sentiment-analysis':
-                if p['label'] == "POS":
-                    col2.success("POSITIVE")
-                    col3.success(f"{ round(p['score'] * 100, 1)}%")
-                elif p['label'] == "NEU":
-                    col2.warning(f"{ p['label'] }")
-                    col3.warning(f"{round(p['score'] * 100, 1)}%")
-                else:
-                    col2.error("NEGATIVE")
-                    col3.error(f"{round(p['score'] * 100, 1)}%")
-            elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
-                if p['label'] == "POSITIVE":
-                    col2.success("POSITIVE")
-                    col3.success(f"{round(p['score'] * 100, 1)}%")
-                else:
-                    col2.error("NEGATIVE")
-                    col3.error(f"{round(p['score'] * 100, 1)}%")
-            elif box == 'twitter-roberta-base-sentiment':
-                if p['label'] == "LABEL_2":
-                    col2.success("POSITIVE")
-                    col3.success(f"{round(p['score'] * 100, 1)}%")
-                elif p['label'] == "LABEL_0":
-                    col2.error("NEGATIVE")
-                    col3.error(f"{round(p['score'] * 100, 1)}%")
-                else:
-                    col2.warning("NEUTRAL")
-                    col3.warning(f"{round(p['score'] * 100, 1)}%")
+        if pline:
+            log = [0] * 4
+            log[1] = tweet
+            for p in predictions:
+                if box == 'bertweet-base-sentiment-analysis':
+                    if p['label'] == "POS":
+                        col1.success(tweet.split("\n")[0][:20])
+                        log[0] = 0
+                        col2.success("POS")
+                        col3.success(f"{ round(p['score'] * 100, 1)}%")
+                        log[2] = ("POS")
+                        log[3] = (f"{ round(p['score'] * 100, 1)}%")
+                    elif p['label'] == "NEU":
+                        col1.warning(tweet.split("\n")[0][:20])
+                        log[0] = 2
+                        col2.warning(f"{ p['label'] }")
+                        col3.warning(f"{round(p['score'] * 100, 1)}%")
+                        log[2] = ("NEU")
+                        log[3] = (f"{round(p['score'] * 100, 1)}%")
+                    else:
+                        log[0] = 1
+                        col1.error(tweet.split("\n")[0][:20])
+                        col2.error("NEG")
+                        col3.error(f"{round(p['score'] * 100, 1)}%")
+                        log[2] = ("NEG")
+                        log[3] = (f"{round(p['score'] * 100, 1)}%")
+                elif box == 'distilbert-base-uncased-finetuned-sst-2-english':
+                    if p['label'] == "POSITIVE":
+                        col1.success(tweet.split("\n")[0][:20])
+                        log[0] = 0
+                        col2.success("POSITIVE")
+                        log[2] = "POSITIVE"
+                        col3.success(f"{round(p['score'] * 100, 1)}%")
+                        log[3] = f"{round(p['score'] * 100, 1)}%"
+                    else:
+                        col2.error("NEGATIVE")
+                        col1.error(tweet.split("\n")[0][:20])
+                        log[2] = ("NEGATIVE")
+                        log[0] = 1
+                        col3.error(f"{round(p['score'] * 100, 1)}%")
+                        log[3] = f"{round(p['score'] * 100, 1)}%"
+                elif box == 'twitter-roberta-base-sentiment':
+                    if p['label'] == "LABEL_2":
+                        log[0] = 0
+                        col1.success(tweet.split("\n")[0][:20])
+                        col2.success("POSITIVE")
+                        col3.success(f"{round(p['score'] * 100, 1)}%")
+                        log[3] = f"{round(p['score'] * 100, 1)}%"
+                        log[2] = "POSITIVE"
+                    elif p['label'] == "LABEL_0":
+                        log[0] = 1
+                        col1.error(tweet.split("\n")[0][:20])
+                        col2.error("NEGATIVE")
+                        col3.error(f"{round(p['score'] * 100, 1)}%")
+                        log[3] = f"{round(p['score'] * 100, 1)}%"
+                        log[2] = "NEGATIVE"
+                    else:
+                        log[0] = 2
+                        col1.warning(tweet.split("\n")[0][:20])
+                        col2.warning("NEUTRAL")
+                        col3.warning(f"{round(p['score'] * 100, 1)}%")
+                        log[3] = f"{round(p['score'] * 100, 1)}%"
+                        log[2] = "NEUTRAL"
+                for a in st.session_state.logs[box][::-1]:
+                    if a[0] == 0:
+                        col1.success(a[1].split("\n")[0][:20])
+                        col2.success(a[2])
+                        col3.success(a[3])
+                    elif a[0] == 1:
+                        col1.error(a[1].split("\n")[0][:20])
+                        col2.error(a[2])
+                        col3.error(a[3])
+                    else:
+                        col1.warning(a[1].split("\n")[0][:20])
+                        col2.warning(a[2])
+                        col3.warning(a[3])
+                st.session_state.logs[box].append(log)
+        else:
+            log = [0] * 6
+            log[1] = tweet
+            if max(predictions) == 0:
+                col1.success(tweet.split("\n")[0][:20])
+                col2.success("NO TOXICITY")
+                col3.success(f"{100 - round(probs[0] * 100, 1)}%")
+                col4.success("N/A")
+                col5.success("N/A")
             else:
-                if predictions[0] == 0:
-                    col2.success("NO TOXICITY")
-                    col3.success(f"{100 - round(probs[0] * 100, 1)}%")
-                    col4.success("N/A")
-                    col5.success("N/A")
+                _max = 0
+                _max2 = 2
+                for i in range(1, len(predictions)):
+                    if probs[i] > probs[_max]:
+                        _max = i
+                    if i > 2 and probs[i] > probs[_max2]:
+                        _max2 = i
+                col1.error(tweet.split("\n")[0][:20])
+                col2.error(st.session_state.labels[_max])
+                col3.error(f"{round(probs[_max] * 100, 1)}%")
+                col4.error(st.session_state.labels[_max2])
+                col5.error(f"{round(probs[_max2] * 100, 1)}%")
+            for a in st.session_state.logs[box][::-1]:
+                if a[0] == 0:
+                    col1.success(a[1].split("\n")[0][:20])
+                    col2.success(a[2])
+                    col3.success(a[3])
+                    col4.success(a[4])
+                    col5.success(a[5])
+                elif a[0] == 1:
+                    col1.error(a[1].split("\n")[0][:20])
+                    col2.error(a[2])
+                    col3.error(a[3])
+                    col4.error(a[4])
+                    col5.error(a[5])
                 else:
-                    col2.error("TOXIC")
-                    col3.error(f"{round(probs[0] * 100, 1)}%")
-                    _max = 1
-                    for i in range(2, len(predictions)):
-                        if probs[i] > probs[_max]:
-                            _max = i
-                    col4.error(labels[_max])
-                    col5.error(f"{round(probs[_max] * 100, 1)}%")
+                    col1.warning(a[1].split("\n")[0][:20])
+                    col2.warning(a[2])
+                    col3.warning(a[3])
+                    col4.warning(a[4])
+                    col5.warning(a[5])
+            st.session_state.logs[box].append(log)

requirements.txt

@@ -1,3 +1,5 @@
 torch
 streamlit
-transformers
+transformers
+numpy
+pandas