Update app.py

app.py

@@ -1,35 +1,29 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
+import gradio as gr
 import joblib
 import numpy as np
 
-app = FastAPI(title="Iris Classification API")
-
+# Load model
 model = joblib.load("model.joblib")
 labels = ["setosa", "versicolor", "virginica"]
 
-class IrisInput(BaseModel):
-    sepal_length: float
-    sepal_width: float
-    petal_length: float
-    petal_width: float
-
-@app.get("/")
-def home():
-    return {"status": "Iris API is running"}
-
-@app.post("/predict")
-def predict(data: IrisInput):
-    X = np.array([[data.sepal_length, data.sepal_width,
-                   data.petal_length, data.petal_width]])
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+    X = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
     probs = model.predict_proba(X)[0]
     idx = probs.argmax()
-    return {
-        "prediction": labels[idx],
-        "confidence": float(probs[idx])
-    }
+    return f"{labels[idx]} (Confidence: {probs[idx]:.2f})"
+
+# Gradio UI
+interface = gr.Interface(
+    fn=predict_iris,
+    inputs=[
+        gr.Number(label="Sepal Length (cm)"),
+        gr.Number(label="Sepal Width (cm)"),
+        gr.Number(label="Petal Length (cm)"),
+        gr.Number(label="Petal Width (cm)")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="🌸 Iris Flower Classification",
+    description="Enter flower measurements to predict the Iris species"
+)
 
-# 🔴 THIS PART IS MANDATORY IN SPACES
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+interface.launch()