Create app.py

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+
+# Load model and scaler
+model = joblib.load("stock_model.pkl")
+scaler = joblib.load("scaler.pkl")
+label_encoder = joblib.load("label_encoder.pkl")
+
+def predict_rating(ticker, pe, de, roe, mcap, dividend):
+    try:
+        # Prepare input for model
+        features = np.array([[float(pe), float(de), float(roe), float(mcap), float(dividend)]])
+        scaled = scaler.transform(features)
+        prediction = model.predict(scaled)
+        rating = label_encoder.inverse_transform(prediction)[0]
+        return f"📈 Stock **{ticker.upper()}** is recommended to: **{rating}**"
+    except Exception as e:
+        return f"❌ Error: {str(e)}"
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_rating,
+    inputs=[
+        gr.Textbox(label="Stock Ticker (e.g., INFY)"),
+        gr.Number(label="PE Ratio"),
+        gr.Number(label="DE Ratio"),
+        gr.Number(label="ROE (%)"),
+        gr.Number(label="Market Cap (₹ Cr)"),
+        gr.Number(label="Dividend Yield (%)")
+    ],
+    outputs="markdown",
+    title="📊 Stock Recommendation - Morningstar Style",
+    description="Enter stock parameters to get a Buy/Hold/Sell recommendation."
+)
+
+iface.launch()