Add application file

app.py

@@ -0,0 +1,67 @@
+import streamlit as st
+import pandas as pd
+from transformers import pipeline
+import matplotlib.pyplot as plt
+import time
+
+# Load the sentiment analysis model
+sentiment_model = pipeline("sentiment-analysis", model="tabularisai/multilingual-sentiment-analysis")
+
+# Function to perform sentiment analysis
+def perform_sentiment_analysis(texts):
+    sentiments = sentiment_model(texts)
+    return sentiments
+
+# Function to plot the sentiment analysis results
+def plot_sentiment_analysis(sentiments):
+    labels = [item['label'] for item in sentiments]
+    label_counts = pd.Series(labels).value_counts()
+
+    fig, ax = plt.subplots()
+    ax.pie(label_counts, labels=label_counts.index, autopct='%1.1f%%', startangle=90)
+    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+
+    st.pyplot(fig)
+
+# Streamlit UI
+st.title("Sentiment Analysis App")
+
+# File upload
+uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
+
+if uploaded_file is not None:
+    # Read the file
+    if uploaded_file.name.endswith(".csv"):
+        df = pd.read_csv(uploaded_file)
+    else:
+        df = pd.read_excel(uploaded_file, engine='openpyxl')
+
+    # Check if 'text' column exists
+    if 'text' not in df.columns:
+        st.warning("Column 'text' not found. Please enter the column name containing the text values.")
+        text_column = st.text_input("Enter the column name containing the text values")
+    else:
+        text_column = 'text'
+
+    if text_column in df.columns:
+        # Display the first few rows of the dataframe
+        st.write("First few rows of the uploaded file:")
+        st.write(df.head())
+
+        # Perform sentiment analysis
+        if st.button("Run Sentiment Analysis"):
+            texts = df[text_column].tolist()
+            progress_bar = st.progress(0)
+
+            # Simulate progress
+            for i in range(100):
+                time.sleep(0.05)
+                progress_bar.progress(i + 1)
+
+            sentiments = perform_sentiment_analysis(texts)
+            st.success("Sentiment analysis completed!")
+
+            # Plot the sentiment analysis results
+            plot_sentiment_analysis(sentiments)
+    else:
+        st.error("The specified column does not exist in the uploaded file.")