Delete app.py

app.py

@@ -1,40 +0,0 @@
-pip install seaborn
-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()