Create app.py

app.py

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+from transformers import pipeline  # For loading Hugging Face pre-trained models
+import gradio as gr               # For creating the user interface
+import pandas as pd              # For handling CSV and Excel files
+import matplotlib.pyplot as plt   # For plotting the pie chart
+
+# Load the pre-trained sentiment-analysis model from Hugging Face
+# This is a binary sentiment model (Positive/Negative)
+sentiment_analyzer = pipeline(
+    "sentiment-analysis",
+    model="distilbert-base-uncased-finetuned-sst-2-english"  # Model fine-tuned on SST-2 for sentiment analysis
+)
+
+# Function to analyze sentiment of a single text input
+def analyze_sentiment(text):
+    # Run the text through the sentiment analyzer
+    result = sentiment_analyzer(text)
+    
+    # Return the sentiment label (Positive/Negative)
+    return result[0]['label']
+
+# Function to analyze a file (CSV or Excel) and return sentiment results + pie chart
+def analyze_file(file):
+    # Read the uploaded file based on its extension (CSV or Excel)
+    if file.name.endswith(".csv"):
+        df = pd.read_csv(file.name)  # Read as CSV
+    elif file.name.endswith(".xlsx"):
+        df = pd.read_excel(file.name)  # Read as Excel
+    else:
+        return "Unsupported file format", None  # Return error message if the file format is unsupported
+
+    # Check if 'Text' column is present in the file
+    if "Text" not in df.columns:
+        return "Missing 'Text' column", None  # Return error if 'Text' column is missing
+
+    # Predict sentiment for each row in the 'Text' column
+    sentiments = [sentiment_analyzer(t)[0]['label'] for t in df["Text"].fillna("")]  # Handle empty cells with fillna("")
+    
+    # Create a DataFrame with 'Text' and corresponding 'Sentiment'
+    df_result = pd.DataFrame({
+        "Text": df["Text"],  # Original text column
+        "Sentiment": sentiments  # Sentiment predictions
+    })
+
+    # Generate a pie chart to show the distribution of sentiments
+    sentiment_counts = df_result["Sentiment"].value_counts()  # Count the occurrences of each sentiment
+    fig, ax = plt.subplots()  # Create a figure and axis for plotting
+    ax.pie(
+        sentiment_counts.values,  # Data for the pie chart
+        labels=sentiment_counts.index,  # Sentiment labels (e.g., 'POSITIVE', 'NEGATIVE')
+        autopct="%1.1f%%",  # Show percentage on the pie chart
+        colors=["#66bb6a", "#ef5350", "#ffee58"][:len(sentiment_counts)]  # Color palette for the pie chart
+    )
+    ax.set_title("Sentiment Distribution")  # Title of the pie chart
+
+    return df_result, fig  # Return the DataFrame and the pie chart figure
+
+# Creating the Gradio interface with two tabs: one for text input and one for file upload
+with gr.Blocks() as demo:
+    gr.Markdown("## 🧠 Text Sentiment Analyzer with Pie Chart")  # Title
+    gr.Markdown("Enter text manually or upload a file with a **Text** column.")  # Description
+
+    # Tab for analyzing a single text input
+    with gr.Tab("🔤 Single Text"):
+        txt = gr.Textbox(label="Text", lines=4)  # Textbox for text input
+        out = gr.Textbox(label="Sentiment")  # Textbox to show sentiment output
+        btn = gr.Button("Analyze")  # Button to trigger analysis
+        btn.click(analyze_sentiment, inputs=txt, outputs=out)  # Trigger analysis when button is clicked
+
+    # Tab for uploading a file (CSV or Excel)
+    with gr.Tab("📁 Upload File"):
+        file_input = gr.File(label="Upload CSV or Excel File")  # File uploader
+        file_output = gr.Dataframe(label="Sentiment Results")  # Table to display the sentiment results
+        chart_output = gr.Plot(label="Sentiment Distribution Pie Chart")  # Pie chart to display sentiment distribution
+        btn2 = gr.Button("Analyze File")  # Button to trigger file analysis
+        btn2.click(analyze_file, inputs=file_input, outputs=[file_output, chart_output])  # Trigger file analysis when button is clicked
+
+# Launch the app
+demo.launch()