Initial commit

README.md

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+---
+title: Streamlit Sentiment Analysis
+emoji: 🐠
+colorFrom: blue
+colorTo: purple
+sdk: docker
+pinned: false
+---

main.py

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+import streamlit as st
+import pandas as pd
+import time
+import plotly.express as px
+from utils.file_handler import validate_file
+from models.sentiment_model import load_model
+
+# Constants
+MAX_FILE_SIZE_MB = 500
+
+# Load sentiment analysis pipeline
+sentiment_pipeline = load_model()
+
+st.title("📊 Sentiment Analysis App")
+
+# File upload
+uploaded_file = st.file_uploader("Upload a CSV file (Max: 500MB)", type=["csv"])
+
+if uploaded_file is not None:
+    if validate_file(uploaded_file, MAX_FILE_SIZE_MB):
+        df = pd.read_csv(uploaded_file)
+        
+        # Check for 'text' column or ask user for correct column
+        if "text" not in df.columns:
+            text_column = st.selectbox("Select the column containing text values", df.columns)
+        else:
+            text_column = "text"
+
+        if st.button("Analyze Sentiment"):
+            st.write("Processing sentiment analysis...")
+            progress_bar = st.progress(0)
+
+            sentiments = []
+            for i, text in enumerate(df[text_column].dropna()):
+                result = sentiment_pipeline(text)
+                sentiments.append(result[0]["label"])
+                progress_bar.progress((i + 1) / len(df))
+                time.sleep(0.1)
+
+            df["Sentiment"] = sentiments
+
+            # Display results
+            st.write("Sentiment Analysis Results:")
+            st.dataframe(df[[text_column, "Sentiment"]])
+
+            # Create pie chart
+            sentiment_counts = df["Sentiment"].value_counts().reset_index()
+            sentiment_counts.columns = ["Sentiment", "Count"]
+            fig = px.pie(sentiment_counts, names="Sentiment", values="Count", title="Sentiment Distribution")
+            st.plotly_chart(fig)
+
+            # Allow CSV download
+            st.download_button("Download Results", df.to_csv(index=False), "sentiment_results.csv", "text/csv")
+    else:
+        st.error("File exceeds the maximum allowed size of 500MB. Please upload a smaller file.")

models/sentiment_model.py

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+from transformers import pipeline
+import streamlit as st
+
+@st.cache_resource
+def load_model():
+    """Load the sentiment analysis model."""
+    return pipeline("sentiment-analysis", model="tabularisai/multilingual-sentiment-analysis")

requirements.txt

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+streamlit
+pandas
+transformers
+torch
+plotly

utils/file_handler.py

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+import streamlit as st
+
+def validate_file(uploaded_file, max_size_mb):
+    """
+    Validates the uploaded file size.
+    :param uploaded_file: The uploaded file object.
+    :param max_size_mb: The maximum allowed file size in MB.
+    :return: True if file size is within limit, else False.
+    """
+    max_size_bytes = max_size_mb * 1024 * 1024  # Convert MB to Bytes
+    if uploaded_file.size > max_size_bytes:
+        return False
+    return True