Create app.py

app.py

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+import gradio as gr
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Function to classify sentiment
+def classify_sentiment(text):
+    # Preprocess the text
+    processed_text = wp(text)
+    # Vectorize the text
+    vectorized_text = vectorization.transform([processed_text])
+    # Predict sentiment using logistic regression model
+    prediction = logistic_model.predict(vectorized_text)[0]
+    # Output sentiment label
+    sentiment_label = output_label(prediction)
+    # Get probabilities for each sentiment class
+    probabilities = logistic_model.predict_proba(vectorized_text)[0]
+
+    # Plot probabilities
+    plt.figure(figsize=(8, 6))
+    sns.barplot(x=["Negative", "Neutral", "Positive"], y=probabilities)
+    plt.xlabel("Sentiment")
+    plt.ylabel("Probability")
+    plt.title("Sentiment Probability Distribution")
+    plt.ylim([0, 1])
+    plt.tight_layout()
+    plt.savefig("sentiment_probabilities.png")
+
+    return sentiment_label, "sentiment_probabilities.png"
+
+# Input and output components for the interface
+inputs = gr.Textbox(lines=10, label="Enter the text you want to analyze:")
+outputs = [
+    gr.Textbox(label="Sentiment Prediction"),
+    gr.Image(label="Sentiment Probability Distribution")
+]
+
+# Create the Gradio interface
+interface = gr.Interface(fn=classify_sentiment, inputs=inputs, outputs=outputs, title="Sentiment Classification", description="Enter a piece of text and analyze its sentiment.")
+interface.launch()