Update app.py

app.py

@@ -1,45 +1,34 @@
 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")
+import joblib
+from huggingface_hub import hf_hub_download
 
-# Load the model
+# Download model from Hugging Face
+model_path = hf_hub_download(repo_id="abhishek/autotrain-iris-xgboost", filename="model.joblib")
 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
+# Prediction 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)
-
+    input_df = pd.DataFrame([{
+        "feat_SepalLengthCm": sepal_length,
+        "feat_SepalWidthCm": sepal_width,
+        "feat_PetalLengthCm": petal_length,
+        "feat_PetalWidthCm": petal_width
+    }])
     prediction = model.predict(input_df)[0]
-    return f"🌸 Predicted species: {prediction}"
+    return prediction
 
 # Gradio interface
-demo = gr.Interface(
+iface = 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"),
+        gr.Slider(4.0, 8.0, label="Sepal Length (cm)"),
+        gr.Slider(2.0, 5.0, label="Sepal Width (cm)"),
+        gr.Slider(1.0, 7.0, label="Petal Length (cm)"),
+        gr.Slider(0.1, 3.0, label="Petal Width (cm)")
     ],
-    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.",
+    outputs="text",
+    title="Iris Flower Classifier 🌸"
 )
 
-demo.launch()
+iface.launch()