Upload 4 files

app.py

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+import gradio as gr
+import joblib
+
+# Load model and vectorizer
+model = joblib.load("model/sentiment_model.pkl")
+vectorizer = joblib.load("model/vectorizer.pkl")
+
+# Prediction function
+def predict_sentiment(text):
+    text_vector = vectorizer.transform([text])
+    prediction = model.predict(text_vector)[0]
+    proba = model.predict_proba(text_vector)[0]
+    
+    sentiment = "Positive" if prediction == 1 else "Negative"
+    return {
+        "Sentiment": sentiment,
+        "Positive Probability": round(proba[1], 2),
+        "Negative Probability": round(proba[0], 2),
+    }
+
+# Gradio UI
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(label="Enter your review or message"),
+    outputs=[
+        gr.Label(label="Sentiment"),
+        gr.Label(label="Positive Probability"),
+        gr.Label(label="Negative Probability"),
+    ],
+    title="Sentiment Analysis",
+    description="Enter a sentence to check if it's Positive or Negative.",
+)
+
+iface.launch()

requirements

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+gradio
+scikit-learn

sentiment_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68e433dc0104aed9bfe940eb80f2c46aa5614121fe13329e4b69916e808b74ce
+size 6814751

vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:58fbe652ee254316b6d687d350d2b0d790ca79dd8001745ec269244fe589ef23
+size 22042389