Create app.py

app.py

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+import logging
+import requests
+import gradio as gr
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Configuration for the model
+API_URL = "https://api-inference.huggingface.co/models/aai540-group3/diabetes-readmission"
+
+# Define constants for the Gradio interface
+AGE_RANGE = (0, 100)
+TIME_IN_HOSPITAL_RANGE = (1, 14)
+NUM_PROCEDURES_RANGE = (0, 10)
+NUM_MEDICATIONS_RANGE = (0, 20)
+NUMBER_DIAGNOSES_RANGE = (1, 10)
+READMITTED_CHOICES = ["<30", ">30", "NO"]
+
+# Define the inference function
+def predict(
+    age, time_in_hospital, num_procedures, num_medications, number_diagnoses,
+    metformin, repaglinide, nateglinide, chlorpropamide, glimepiride,
+    glipizide, glyburide, pioglitazone, rosiglitazone, acarbose, insulin,
+    readmitted
+):
+    # Create a dictionary from the input features
+    input_data = {
+        "age": age,
+        "time_in_hospital": time_in_hospital,
+        "num_procedures": num_procedures,
+        "num_medications": num_medications,
+        "number_diagnoses": number_diagnoses,
+        "metformin": metformin,
+        "repaglinide": repaglinide,
+        "nateglinide": nateglinide,
+        "chlorpropamide": chlorpropamide,
+        "glimepiride": glimepiride,
+        "glipizide": glipizide,
+        "glyburide": glyburide,
+        "pioglitazone": pioglitazone,
+        "rosiglitazone": rosiglitazone,
+        "acarbose": acarbose,
+        "insulin": insulin,
+        "readmitted": readmitted
+    }
+
+    try:
+        # Make a request to the Hugging Face inference API
+        response = requests.post(API_URL, json={"inputs": input_data})
+        response.raise_for_status()  # Raise an error for bad responses
+
+        prediction = response.json()
+        logger.info(f"Prediction received: {prediction}")
+        return f"<h1 style='font-size: 48px; color: green;'>Prediction: {prediction}</h1>"
+
+    except requests.exceptions.RequestException as e:
+        logger.error(f"Error in API request: {e}")
+        return "<h1 style='font-size: 48px; color: red;'>Error in prediction</h1>"
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Slider(minimum=AGE_RANGE[0], maximum=AGE_RANGE[1], label="Age"),
+        gr.Slider(minimum=TIME_IN_HOSPITAL_RANGE[0], maximum=TIME_IN_HOSPITAL_RANGE[1], label="Time in Hospital (days)"),
+        gr.Slider(minimum=NUM_PROCEDURES_RANGE[0], maximum=NUM_PROCEDURES_RANGE[1], label="Number of Procedures"),
+        gr.Slider(minimum=NUM_MEDICATIONS_RANGE[0], maximum=NUM_MEDICATIONS_RANGE[1], label="Number of Medications"),
+        gr.Slider(minimum=NUMBER_DIAGNOSES_RANGE[0], maximum=NUMBER_DIAGNOSES_RANGE[1], label="Number of Diagnoses"),
+        gr.Checkbox(label="Metformin"),
+        gr.Checkbox(label="Repaglinide"),
+        gr.Checkbox(label="Nateglinide"),
+        gr.Checkbox(label="Chlorpropamide"),
+        gr.Checkbox(label="Glimepiride"),
+        gr.Checkbox(label="Glipizide"),
+        gr.Checkbox(label="Glyburide"),
+        gr.Checkbox(label="Pioglitazone"),
+        gr.Checkbox(label="Rosiglitazone"),
+        gr.Checkbox(label="Acarbose"),
+        gr.Checkbox(label="Insulin"),
+        gr.Radio(choices=READMITTED_CHOICES, label="Readmitted")
+    ],
+    outputs=gr.HTML(label="Prediction"),
+    title="Diabetes Readmission Prediction",
+    description="Enter patient data to predict the likelihood of readmission."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    iface.launch()