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app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[8]:
+
+
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[6]:
+
+
+# app.py
+import gradio as gr
+import joblib
+import json
+import numpy as np
+
+# Load the model and scaler
+model = joblib.load('xgboost_breast_cancer_model.joblib')
+scaler = joblib.load('scaler.joblib')
+
+# Load feature names
+with open('feature_names.json', 'r') as f:
+    feature_names = json.load(f)
+
+def predict_cancer(*features):
+    # Convert inputs to numpy array
+    input_data = np.array(features).reshape(1, -1)
+    
+    # Scale the input data
+    scaled_input = scaler.transform(input_data)
+    
+    # Make prediction
+    prediction_proba = model.predict_proba(scaled_input)[0, 1]
+    
+    # Apply threshold
+    prediction = "Malignant" if prediction_proba >= 0.4 else "Benign"
+    
+    return f"Prediction: {prediction}\nProbability of being malignant: {prediction_proba:.2f}"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_cancer,
+    inputs=[gr.Number(label=name) for name in feature_names],
+    outputs="text",
+    title="Breast Cancer Prediction",
+    description="Enter the feature values to predict whether a breast mass is benign or malignant."
+)
+
+iface.launch()
+
+
+# In[ ]:
+
+
+
+
+
+
+# In[ ]:
+
+
+
+

feature_names.json

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+["concave points_worst", "perimeter_worst", "concave points_mean", "radius_worst", "perimeter_mean", "area_worst"]

scaler.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:18cb378aa97b540ef898aee2b95462b03553acb9d675c6afd1b36f1a1ad74fa5
+size 1181

xgboost_breast_cancer_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae834ee3f7ad316082560834a1ab63fb545af1cbc9c133dd6ab6abaa5c4802b8
+size 114459