Create app.py

app.py

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+import gradio as gr
+import pickle
+import numpy as np
+
+# Load your model from the .pkl file
+with open('model.pkl', 'rb') as model_file:
+    model = pickle.load(model_file)
+
+# Define the prediction function
+def predict_disease(age, gender, fever, cough, fatigue, difficulty_breathing, blood_pressure, cholesterol_level):
+    # Convert inputs to numerical or categorical as your model expects
+    gender = 0 if gender == "Male" else 1
+    fever = 1 if fever == "Yes" else 0
+    cough = 1 if cough == "Yes" else 0
+    fatigue = 1 if fatigue == "Yes" else 0
+    difficulty_breathing = 1 if difficulty_breathing == "Yes" else 0
+    blood_pressure_mapping = {"Low": 0, "Normal": 1, "High": 2}
+    cholesterol_level_mapping = {"Low": 0, "Normal": 1, "High": 2}
+    
+    blood_pressure = blood_pressure_mapping[blood_pressure]
+    cholesterol_level = cholesterol_level_mapping[cholesterol_level]
+
+    # Assuming your model takes input as a NumPy array or similar format
+    input_features = np.array([[age, gender, fever, cough, fatigue, difficulty_breathing, blood_pressure, cholesterol_level]])
+
+    # Get the prediction
+    prediction = model.predict(input_features)
+    return prediction[0]  # Return the predicted disease
+
+# Gradio interface
+inputs = [
+    gr.inputs.Number(label="Age"),
+    gr.inputs.Radio(choices=["Male", "Female"], label="Gender"),
+    gr.inputs.Radio(choices=["Yes", "No"], label="Fever"),
+    gr.inputs.Radio(choices=["Yes", "No"], label="Cough"),
+    gr.inputs.Radio(choices=["Yes", "No"], label="Fatigue"),
+    gr.inputs.Radio(choices=["Yes", "No"], label="Difficulty Breathing"),
+    gr.inputs.Dropdown(choices=["Low", "Normal", "High"], label="Blood Pressure"),
+    gr.inputs.Dropdown(choices=["Low", "Normal", "High"], label="Cholesterol Level")
+]
+
+outputs = gr.outputs.Textbox(label="Predicted Disease")
+
+# Create and launch the app
+app = gr.Interface(fn=predict_disease, inputs=inputs, outputs=outputs, title="Symptom to Disease Prediction")
+
+app.launch()