api change

app.py

@@ -9,12 +9,8 @@ def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
     arr = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
     pred = model.predict(arr)[0]
     proba = model.predict_proba(arr)[0]
-    return {
-        "predicted_class": str(target_names[pred]),
-        "probabilities": {str(target_names[i]): float(proba[i]) for i in range(len(target_names))}
-    }
+    return str(target_names[pred]), {str(target_names[i]): float(proba[i]) for i in range(len(target_names))}
 
-# Gradio UI
 with gr.Blocks() as demo:
     gr.Markdown("# 🌸 Iris Detector — KNN Classifier (k=5)")
     gr.Markdown("Enter 4 iris flower measurements below to predict the species:")
@@ -25,39 +21,42 @@ with gr.Blocks() as demo:
             sepal_width = gr.Number(label="Sepal Width (cm)")
             petal_length = gr.Number(label="Petal Length (cm)")
             petal_width = gr.Number(label="Petal Width (cm)")
+
             predict_btn = gr.Button("Predict")
-            output = gr.JSON(label="Prediction")
+            output_class = gr.Label(label="Predicted Class")
+            output_proba = gr.JSON(label="Probabilities")
 
             predict_btn.click(
                 fn=predict_iris,
                 inputs=[sepal_length, sepal_width, petal_length, petal_width],
-                outputs=[output]
+                outputs=[output_class, output_proba]
             )
 
         with gr.Column():
-            gr.Markdown('''## 📖 API Usage Guide
+            gr.Markdown("""## 📖 API Usage Guide
 You can call this model programmatically using the Gradio external API.
 
 **Endpoint:**
 ```
-POST https://huggingface.co/spaces/tofighi/iris-detector/run/predict
+POST https://huggingface.co/spaces/<username>/iris-detector/run/predict
 ```
 
 **Request Body Example:**
 ```json
 {
-  "data": [[5.1, 3.5, 1.4, 0.2]]
+  "data": [5.1, 3.5, 1.4, 0.2]
 }
 ```
 
 **Python Example:**
 ```python
 import requests
-url = "https://huggingface.co/spaces/tofighi/iris-detector/run/predict"
-payload = {"data": [[5.1, 3.5, 1.4, 0.2]]}
+
+url = "https://huggingface.co/spaces/<username>/iris-detector/run/predict"
+payload = {"data": [5.1, 3.5, 1.4, 0.2]}
 resp = requests.post(url, json=payload)
 print(resp.json())
 ```
-''')
+""")
 
-demo.launch(share=True)
+demo.launch()