Initial commit for Gradio Diabetes App

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/GRADIO DEMO.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/Project_Default.xml

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+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
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+      <option name="ignoredPackages">
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+            <item index="0" class="java.lang.String" itemvalue="tensorflow" />
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+    <inspection_tool class="PyPep8Inspection" enabled="true" level="WEAK WARNING" enabled_by_default="true">
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.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (GRADIO DEMO)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/GRADIO DEMO.iml" filepath="$PROJECT_DIR$/.idea/GRADIO DEMO.iml" />
+    </modules>
+  </component>
+</project>

README.md

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+# 🩺 Diabetes Risk Predictor
+
+This app uses a logistic regression model trained on the Pima Indians Diabetes dataset to predict whether a patient is diabetic based on medical features.
+
+##  How to Use
+
+- Clone or download the repository
+- Install dependencies: `pip install -r requirements.txt`
+- Run the app: `python diabetes_app.py`
+
+Built using Gradio and scikit-learn.

app.py

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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import gradio as gr
+
+# Load the dataset
+url = "https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv"
+df = pd.read_csv(url)
+
+
+# Preparing the data
+X = df.drop("Outcome", axis=1)
+y = df["Outcome"]
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Training the model
+model = RandomForestClassifier()
+model.fit(X_train, y_train)
+
+# Check accuracy in console
+y_pred = model.predict(X_test)
+print("Model trained. Accuracy on test data:", accuracy_score(y_test, y_pred))
+
+# Gradio prediction function
+def predict_diabetes(Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, DiabetesPedigreeFunction, Age):
+    input_data = [[Pregnancies, Glucose, BloodPressure, SkinThickness,Insulin, BMI, DiabetesPedigreeFunction, Age]]
+    prediction = model.predict(input_data)[0]
+    return "Diabetes" if prediction == 1 else "Not Diabetic"
+
+# Gradio Interface
+with gr.Blocks() as iface:
+    gr.Markdown("# 🩺 Diabetes Risk Predictor")
+    gr.Markdown("Enter medical details to predict whether the patient is diabetic.")
+
+    with gr.Row():
+        Pregnancies = gr.Number(label="Pregnancies")
+        Glucose = gr.Number(label="Glucose")
+        BloodPressure = gr.Number(label="BloodPressure")
+        SkinThickness = gr.Number(label="SkinThickness")
+        Insulin = gr.Number(label="Insulin")
+        BMI = gr.Number(label="BMI")
+        DiabetesPedigreeFunction = gr.Number(label="DiabetesPedigreeFunction")
+        Age = gr.Number(label="Age")
+
+    predict_btn = gr.Button("Predict")
+    output = gr.Textbox(label="Prediction")
+
+    def on_click_fn(*args):
+        return predict_diabetes(*args)
+
+    predict_btn.click(
+        on_click_fn,
+        inputs=[
+            Pregnancies, Glucose, BloodPressure, SkinThickness,
+            Insulin, BMI, DiabetesPedigreeFunction, Age
+        ],
+        outputs=output
+    )
+
+iface.launch(share=True)
+