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app.py

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+# app.py
+import gradio as gr
+import joblib
+
+# --- 1. Load the Model and Vectorizer ---
+# Load the trained model and the TF-IDF vectorizer from disk.
+try:
+    model = joblib.load('logistic_regression_model.joblib')
+    vectorizer = joblib.load('tfidf_vectorizer.joblib')
+    print("Model and vectorizer loaded successfully.")
+except FileNotFoundError:
+    print("Error: Model or vectorizer files not found. Make sure they are in the same directory.")
+    # We'll let the app crash if files aren't found, as it can't run without them.
+    raise
+
+# --- 2. Define the Prediction Function ---
+# This function will take a text input and return the predicted sentiment.
+def predict_sentiment(text):
+    # Transform the input text using the loaded vectorizer.
+    vectorized_text = vectorizer.transform([text])
+    # Make a prediction using the loaded model.
+    prediction = model.predict(vectorized_text)
+    # Return the first element of the prediction array.
+    return prediction[0]
+
+# --- 3. Create and Launch the Gradio Interface ---
+# Define the user interface for the app.
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=5, label="Enter a sentence to classify"),
+    outputs=gr.Label(label="Predicted Sentiment"),
+    title="Simple Sentiment Analysis",
+    description="A simple sentiment analysis model that classifies text as positive, negative, or neutral (depending on your training).",
+    allow_flagging="never"
+)
+
+# Launch the app. This will start a web server.
+iface.launch()

logistic_regression_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:96743d45beea8554db01959ccef336274405fac143a6a7b4d30611838d4bdf0e
+size 1200911

requirements.txt

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+gradio
+repandas
+nltk
+sklearn
+matplotlib
+seaborn
+

tfidf_vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2669ffdfad71164a8faac2ec9fc6daad31c07c01e47bdc4b08bfac6ee211d55b
+size 1995608