Create app.py

app.py

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+#1. Importing Lib
+
+import gradio as gr
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score,r2_score
+
+#2. Data Preprocessing
+
+df=pd.read_csv("heart.csv")
+
+# Spliting data into x and y (independent/dependent)
+
+x=df.drop("target",axis=1)
+y=df["target"]
+
+#3. Modeling Part
+
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)
+model=LogisticRegression()
+
+model.fit(x_train,y_train)
+model.fit(x_test,y_test)
+
+x_predict=model.predict(x_train)
+x_accuracy=accuracy_score(x_predict,y_train)
+
+y_predict=model.predict(x_test)
+y_accuracy=accuracy_score(y_predict,y_test)
+
+#4. UI for Model using gradio
+
+# Function to make predictions
+def predict_heart_disease(age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal):
+    input_data = np.array([[age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, ca, thal]])
+    prediction = model.predict(input_data)
+
+    if prediction[0] == 0:
+        return "Person does not have Heart Disease"
+    else:
+        return "Person has Heart Disease"
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=predict_heart_disease,  # Function that makes predictions
+    inputs=[
+        gr.Slider(minimum=29, maximum=77, step=1, label="Age"),
+        gr.Dropdown([0, 1], label="Sex (0 = Female, 1 = Male)"),
+        gr.Dropdown([0, 1, 2, 3], label="Chest Pain Type (cp)"),
+        gr.Slider(minimum=90, maximum=200, step=1, label="Resting Blood Pressure (trestbps)"),
+        gr.Slider(minimum=120, maximum=600, step=1, label="Serum Cholesterol (chol)"),
+        gr.Dropdown([0, 1], label="Fasting Blood Sugar (fbs)"),
+        gr.Dropdown([0, 1,2], label="Resting Electrocardiographic Results (restecg)"),
+        gr.Slider(minimum=70, maximum=202, step=1, label="Maximum Heart Rate Achieved (thalach)"),
+        gr.Dropdown([0, 1], label="Exercise Induced Angina (exang)"),
+        gr.Slider(minimum=0.0, maximum=6.2, step=0.1, label="Oldpeak (depression induced by exercise)"),
+        gr.Dropdown([0, 1, 2], label="Slope of the Peak Exercise ST Segment (slope)"),
+        gr.Dropdown([0, 1, 2,3,4], label="Number of Major Vessels Colored by Fluoroscopy (ca)"),
+        gr.Dropdown([0, 1, 2, 3], label="Thalassemia (thal)")
+    ],  # Input fields
+    outputs="text"  # Output the prediction result as text
+)
+
+# Launch the Gradio UI
+iface.launch()