Create app.py

app.py

@@ -0,0 +1,45 @@
+import gradio as gr
+from huggingface_hub import hf_hub_download
+import joblib
+import pandas as pd
+import json
+
+# Load model and config from Hugging Face
+repo_id = "abhishek/autotrain-iris-xgboost"
+
+model_path = hf_hub_download(repo_id=repo_id, filename="model.joblib")
+config_path = hf_hub_download(repo_id=repo_id, filename="config.json")
+
+# Load the model
+model = joblib.load(model_path)
+
+# Load the config to get feature names
+with open(config_path, "r") as f:
+    config = json.load(f)
+
+feature_names = config["features"]
+
+# Inference function
+def predict(sepal_length, sepal_width, petal_length, petal_width):
+    input_df = pd.DataFrame([[
+        sepal_length, sepal_width, petal_length, petal_width
+    ]], columns=feature_names)
+
+    prediction = model.predict(input_df)[0]
+    return f"🌸 Predicted species: {prediction}"
+
+# Gradio interface
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Slider(4.0, 8.0, step=0.1, label="Sepal Length"),
+        gr.Slider(2.0, 5.0, step=0.1, label="Sepal Width"),
+        gr.Slider(1.0, 7.0, step=0.1, label="Petal Length"),
+        gr.Slider(0.1, 3.0, step=0.1, label="Petal Width"),
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="🌸 Iris Flower Classifier",
+    description="Enter flower measurements to predict the species using a model trained with AutoTrain on Hugging Face.",
+)
+
+demo.launch()