Create app.py

app.py

@@ -0,0 +1,59 @@
+# app.py
+import re, string
+import joblib
+import numpy as np
+import gradio as gr
+
+# -----------------------------
+# 1️⃣ Load model and vectorizer
+# -----------------------------
+model = joblib.load("svm_model.pkl")
+tfidf = joblib.load("tfidf_vectorizer.pkl")
+
+# -----------------------------
+# 2️⃣ Preprocess function
+# -----------------------------
+def preprocess(text):
+    text = str(text).lower()
+    text = re.sub(f"[{string.punctuation}]", "", text)
+    text = re.sub(r"\d+", "", text)
+    text = text.strip()
+    return text
+
+# -----------------------------
+# 3️⃣ Prediction function
+# -----------------------------
+def predict_sentiment(review):
+    if not review:
+        return "Error: No review provided", 0.0
+    cleaned = preprocess(review)
+    vectorized = tfidf.transform([cleaned])
+    
+    pred = model.predict(vectorized)[0]
+    sentiment = "positive" if pred == 1 else "negative"
+    
+    confidence = model.decision_function(vectorized)[0]
+    confidence = 1 / (1 + np.exp(-confidence))  # sigmoid for probability-like score
+    
+    return sentiment, round(float(confidence)*100, 2)
+
+# -----------------------------
+# 4️⃣ Gradio UI
+# -----------------------------
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(label="Write a review here...", lines=5, placeholder="Type your review..."),
+    outputs=[
+        gr.Label(label="Predicted Sentiment"),
+        gr.Number(label="Confidence (%)")
+    ],
+    title="Amazon Review Sentiment Analysis",
+    description="Enter an Amazon product review and get the predicted sentiment along with confidence score.",
+    theme="default"
+)
+
+# -----------------------------
+# 5️⃣ Launch
+# -----------------------------
+if __name__ == "__main__":
+    iface.launch()