Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from datasets import load_dataset
+from transformers import pipeline
+
+# Load Enron Dataset
+@st.cache
+def load_data():
+    dataset = load_dataset('enron_email')
+    return dataset['train']
+
+data = load_data()
+
+# Load models
+sentiment_model = pipeline('sentiment-analysis')
+ner_model = pipeline('ner', aggregation_strategy="simple")
+topic_model = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')
+
+# Streamlit UI
+st.title('Enron Email Analysis')
+
+st.sidebar.title('Options')
+email_id = st.sidebar.selectbox('Select Email ID', range(len(data)))
+
+email_text = data[email_id]['text']
+st.write(f"## Email Content\n{email_text}")
+
+# Sentiment Analysis
+st.write("## Sentiment Analysis")
+sentiment = sentiment_model(email_text)
+st.write(sentiment)
+
+# Named Entity Recognition
+st.write("## Named Entity Recognition (NER)")
+entities = ner_model(email_text)
+st.write(entities)
+
+# Topic Modeling (Zero-Shot Classification)
+st.write("## Topic Modeling")
+labels = ['business', 'personal', 'financial', 'legal', 'politics']
+topics = topic_model(email_text, candidate_labels=labels)
+st.write(topics)