Update app.py

app.py

@@ -1,34 +1,32 @@
 import gradio as gr
-import pandas as pd
 import joblib
+import pandas as pd
 from huggingface_hub import hf_hub_download
 
-# Download model from Hugging Face
+# Download model from Hugging Face Hub
 model_path = hf_hub_download(repo_id="abhishek/autotrain-iris-xgboost", filename="model.joblib")
 model = joblib.load(model_path)
 
-# Prediction function
+# Input labels expected by the model
+feature_names = ['feat_SepalLengthCm', 'feat_SepalWidthCm', 'feat_PetalLengthCm', 'feat_PetalWidthCm']
+
 def predict(sepal_length, sepal_width, petal_length, petal_width):
-    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 prediction
+    data = pd.DataFrame([[sepal_length, sepal_width, petal_length, petal_width]], columns=feature_names)
+    prediction = model.predict(data)[0]
+    return f"Predicted Iris Class: {prediction}"
 
 # Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=[
-        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)")
+        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="text",
-    title="Iris Flower Classifier 🌸"
+    outputs=gr.Textbox(label="Prediction"),
+    title="Iris Species Predictor 🌸",
+    description="Enter flower features to predict the Iris species using a model trained with AutoTrain Tabular."
 )
 
 iface.launch()