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app.py

@@ -1,7 +1,88 @@
 import gradio as gr
+import numpy as np
+import joblib
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Load trained KNN model
+model, target_names = joblib.load("iris_knn.pkl")
+
+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 str(target_names[pred]), {str(target_names[i]): float(proba[i]) for i in range(len(target_names))}
+
+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:")
+
+    with gr.Row():
+        with gr.Column():
+            sepal_length = gr.Number(label="Sepal Length (cm)")
+            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_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_class, output_proba]
+            )
+
+        with gr.Column():
+            gr.Markdown(
+"""
+## 📖 Iris Detector API Usage (FastAPI)
+
+Your predictions can also be made programmatically using the FastAPI backend deployed at:
+
+### **API Endpoint**
+```
+POST https://tofighi-iris-detector-api.hf.space/predict
+```
+
+---
+
+### **📌 JSON Request Example**
+```json
+{
+  "sepal_length": 5.1,
+  "sepal_width": 3.5,
+  "petal_length": 1.4,
+  "petal_width": 0.2
+}
+```
+
+---
+
+### **🐍 Python Example**
+```python
+import requests
+
+url = "https://tofighi-iris-detector-api.hf.space/predict"
+
+data = {
+    "sepal_length": 5.1,
+    "sepal_width": 3.5,
+    "petal_length": 1.4,
+    "petal_width": 0.2
+}
+
+resp = requests.post(url, json=data)
+print(resp.json())
+```
+
+---
+
+### **💻 cURL Example**
+```bash
+curl -X POST "https://tofighi-iris-detector-api.hf.space/predict" \
+     -H "Content-Type: application/json" \
+     -d '{"sepal_length":5.1,"sepal_width":3.5,"petal_length":1.4,"petal_width":0.2}'
+```
+""")
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()

iris_knn.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:af2f61d7c8a95f19394fb776308cd844f5ce159bff513b77e808efe98241100b
+size 14315

requirements.txt

@@ -0,0 +1,4 @@
+gradio
+numpy
+scikit-learn
+joblib