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app.py

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+# pip install streamlit,
+# pip install pyngrok==4.1.1,
+# pip install vaderSentiment,
+# pip install transformers
+
+import os
+
+os.system('pip install --upgrade pip')
+os.system('pip install textblob')
+os.system('pip install vaderSentiment')
+os.system('pip install transformers')
+os.system('pip install numpy')
+os.system('pip install scipy')
+os.system('pip install streamlit')
+os.system('pip install pandas')
+os.system('pip install altair')
+os.system('pip install vaderSentiment')
+os.system('pip install torch')
+os.system('pip install pyngrok')
+os.system('pip install streamlit --upgrade')
+
+import torch
+import streamlit as st  
+from textblob import TextBlob
+import pandas as pd
+import altair as alt
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+from transformers import AutoModelForSequenceClassification
+from transformers import TFAutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+import streamlit as st
+
+
+
+# Requirements
+model_path = f"MaryanneMuchai/twitter-finetuned-model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+config = AutoConfig.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+
+def sentiment_analysis(text):
+    text = preprocess(text)
+
+    # PyTorch-based models
+    encoded_input = tokenizer(text, return_tensors='pt')
+    output = model(**encoded_input)
+    scores_ = output[0][0].detach().numpy()
+    scores_ = softmax(scores_)
+    
+    # Format output dict of scores
+    labels = ['Negative', 'Neutral', 'Positive']
+    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
+    
+    return scores
+def convert_to_df(sentiment):
+	sentiment_dict = {'polarity':sentiment.polarity,'subjectivity':sentiment.subjectivity}
+	sentiment_df = pd.DataFrame(sentiment_dict.items(),columns=['metric','value'])
+	return sentiment_df
+
+def main():
+	st.title("Sentiment Analysis NLP App")
+	st.subheader("Streamlit Projects")
+
+	menu = ["Home","About"]
+	choice = st.sidebar.selectbox("Menu",menu)
+
+	if choice == "Home":
+		st.subheader("Home")
+		with st.form(key='nlpForm'):
+			raw_text = st.text_area("Enter Text Here")
+			submit_button = st.form_submit_button(label='Analyze')
+
+		# layout
+		col1,col2 = st.columns(2)
+		if submit_button:
+
+			with col1:
+				st.info("Results")
+				sentiment = TextBlob(raw_text).sentiment
+				st.write(sentiment)
+
+				# Emoji
+				if sentiment.polarity > 0:
+					st.markdown("Sentiment:: Positive :smiley: ")
+				elif sentiment.polarity < 0:
+					st.markdown("Sentiment:: Negative :angry: ")
+				else:
+					st.markdown("Sentiment:: Neutral ?? ")
+
+				# Dataframe
+				result_df = convert_to_df(sentiment)
+				st.dataframe(result_df)
+
+				# Visualization
+				c = alt.Chart(result_df).mark_bar().encode(
+					x='metric',
+					y='value',
+					color='metric')
+				st.altair_chart(c,use_container_width=True)
+
+
+
+			with col2:
+				st.info("Token Sentiment")
+
+				token_sentiments = sentiment_analysis(raw_text)
+				st.write(token_sentiments)
+
+
+
+
+
+
+	else:
+		st.subheader("About")
+
+
+if __name__ == '__main__':
+	main()
+ 
+
+
+# Expose the app publicly using ngrok
+# from pyngrok import ngrok
+# public_url = ngrok.connect(port='8501')
+# public_url
+
+# !streamlit run --server.port 8501 Sentiment_Analysis.py
+
+# !pip freeze > requirements.txt