Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.feature_extraction.text import CountVectorizer
+from transformers import pipeline
+from bertopic import BERTopic
+
+# Emotion classification pipeline (can use AraBERT or any emotion classifier)
+emotion_classifier = pipeline("text-classification", model="arpanghoshal/bert-base-uncased-emotion")
+
+# Function to process CSV file and return emotion and topic model
+def process_file(uploaded_file):
+    # Load CSV
+    df = pd.read_csv(uploaded_file)
+    
+    # Display basic info about the CSV
+    st.write("CSV Loaded Successfully!")
+    st.write(f"Data Preview: {df.head()}")
+    
+    # Preprocess the text: assuming the CSV has a 'text' column
+    texts = df['text'].dropna().tolist()  # Modify this according to your column name
+
+    # Emotion Classification: Classify emotions for each text
+    emotions = [emotion_classifier(text)[0]['label'] for text in texts]
+    df['emotion'] = emotions
+    
+    # Topic Modeling using BERTopic (install bertopic first if not installed)
+    topic_model = BERTopic()
+    topics, _ = topic_model.fit_transform(texts)
+    df['topic'] = topics
+    
+    # Display the results
+    st.write("Emotions classified for each entry:")
+    st.write(df[['text', 'emotion', 'topic']])
+    
+    return df
+
+# Streamlit App
+st.title("Topic Modeling & Emotion Classification")
+st.write("Upload a CSV file to perform topic modeling and emotion classification on the text.")
+
+# File upload widget
+uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    # Process the file
+    result_df = process_file(uploaded_file)