updated app.py

app.py

@@ -0,0 +1,205 @@
+import gradio as gr
+import xgboost as xgb
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, confusion_matrix
+import plotly.express as px
+import plotly.graph_objects as go
+import json
+
+class DiabetesGame:
+    def __init__(self):
+        self.data = self.load_data()
+        self.model = self.train_model()
+        self.user_score = 0
+        self.total_attempts = 0
+        self.tips_database = {
+            "glucose": "Normal fasting blood glucose levels are less than 100 mg/dL. Levels between 100-125 mg/dL indicate prediabetes.",
+            "bmi": "A BMI between 18.5 and 24.9 is considered healthy. BMI over 30 indicates obesity, a risk factor for diabetes.",
+            "blood_pressure": "Normal blood pressure is usually below 120/80 mmHg. High blood pressure often co-occurs with diabetes.",
+            "age": "Type 2 diabetes risk increases with age, particularly after 45 years.",
+            "pregnancies": "Gestational diabetes during pregnancy increases future diabetes risk.",
+            "lifestyle": "Regular exercise and a balanced diet can significantly reduce diabetes risk.",
+            "family_history": "Having a parent or sibling with diabetes increases your risk.",
+            "general": "Early detection and lifestyle changes can prevent or delay type 2 diabetes."
+        }
+        
+    def load_data(self):
+        return pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv')
+    
+    def train_model(self):
+        X = self.data.drop(columns=['Outcome'])
+        y = self.data['Outcome']
+        X_train, _, y_train, _ = train_test_split(X, y, test_size=0.2, random_state=42)
+        
+        model = xgb.XGBClassifier(
+            eval_metric='logloss',
+            max_depth=6,
+            learning_rate=0.1,
+            n_estimators=100,
+            random_state=42
+        )
+        model.fit(X_train, y_train)
+        return model
+    
+    def create_radar_chart(self, values):
+        categories = ['Pregnancies', 'Glucose', 'Blood Pressure', 'Skin Thickness',
+                     'Insulin', 'BMI', 'Diabetes Pedigree', 'Age']
+        
+        # Normalize values for radar chart
+        max_values = [17, 200, 122, 99, 846, 67.1, 2.42, 81]
+        normalized_values = [v/m for v, m in zip(values, max_values)]
+        
+        fig = go.Figure()
+        fig.add_trace(go.Scatterpolar(
+            r=normalized_values,
+            theta=categories,
+            fill='toself',
+            name='Patient Values'
+        ))
+        
+        fig.update_layout(
+            polar=dict(
+                radialaxis=dict(
+                    visible=True,
+                    range=[0, 1]
+                )),
+            showlegend=True,
+            title="Patient Risk Factors Radar Chart"
+        )
+        return fig
+    
+    def create_feature_importance_plot(self):
+        importance_df = pd.DataFrame({
+            'Feature': ['Pregnancies', 'Glucose', 'Blood Pressure', 'Skin Thickness',
+                       'Insulin', 'BMI', 'Diabetes Pedigree', 'Age'],
+            'Importance': self.model.feature_importances_
+        })
+        importance_df = importance_df.sort_values('Importance', ascending=True)
+        
+        fig = px.bar(importance_df, x='Importance', y='Feature',
+                    orientation='h',
+                    title='Feature Importance in Diagnosis')
+        fig.update_layout(height=400)
+        return fig
+    
+    def get_relevant_tips(self, values):
+        tips = []
+        
+        # Add tips based on values
+        if values[1] > 140:  # High glucose
+            tips.append(("⚠️ High Glucose", self.tips_database["glucose"]))
+        if values[5] > 30:   # High BMI
+            tips.append(("⚠️ High BMI", self.tips_database["bmi"]))
+        if values[2] > 90:   # High blood pressure
+            tips.append(("⚠️ High Blood Pressure", self.tips_database["blood_pressure"]))
+        
+        # Always add a random general tip
+        general_tips = [self.tips_database["lifestyle"], 
+                       self.tips_database["family_history"],
+                       self.tips_database["general"]]
+        tips.append(("💡 Health Tip", np.random.choice(general_tips)))
+        
+        return "\n\n".join([f"{title}\n{content}" for title, content in tips])
+    
+    def predict_and_play(self, pregnancies, glucose, blood_pressure, skin_thickness, 
+                        insulin, bmi, diabetes_pedigree, age, user_diagnosis):
+        
+        values = [pregnancies, glucose, blood_pressure, skin_thickness,
+                 insulin, bmi, diabetes_pedigree, age]
+        
+        # Make prediction
+        user_input = np.array(values).reshape(1, -1)
+        prediction = self.model.predict(user_input)[0]
+        probability = self.model.predict_proba(user_input)[0][1] * 100
+        
+        # Update score
+        self.total_attempts += 1
+        correct = (user_diagnosis == "Yes" and prediction == 1) or \
+                 (user_diagnosis == "No" and prediction == 0)
+        if correct:
+            self.user_score += 1
+        
+        # Create visualizations
+        radar_chart = self.create_radar_chart(values)
+        importance_plot = self.create_feature_importance_plot()
+        
+        # Generate result message
+        accuracy = (self.user_score / self.total_attempts) * 100 if self.total_attempts > 0 else 0
+        
+        result = f"""{'🎉 Correct!' if correct else '❌ Incorrect'}\n
+Model Prediction: {'Diabetes Risk Detected' if prediction == 1 else 'No Significant Risk'}\n
+Confidence: {probability:.1f}%\n
+Your Score: {self.user_score}/{self.total_attempts} ({accuracy:.1f}% accuracy)
+"""
+        
+        # Get relevant tips
+        tips = self.get_relevant_tips(values)
+        
+        return result, radar_chart, importance_plot, tips
+
+# Initialize game
+game = DiabetesGame()
+
+# Create the interface
+with gr.Blocks(theme=gr.themes.Soft()) as interface:
+    gr.Markdown("""
+    # 🏥 Interactive Diabetes Diagnosis Game
+    
+    Test your medical diagnosis skills! Analyze patient data and try to predict diabetes risk.
+    Your score will be tracked as you play.
+    
+    ## How to Play:
+    1. Adjust the patient parameters using the sliders
+    2. Make your diagnosis (Yes/No for diabetes risk)
+    3. Submit to see if you matched the model's prediction
+    4. Learn from the visualizations and tips
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            pregnancies = gr.Slider(0, 17, step=1, label="Pregnancies")
+            glucose = gr.Slider(0, 200, value=120, step=1, label="Glucose Level")
+            blood_pressure = gr.Slider(0, 122, value=70, step=1, label="Blood Pressure")
+            skin_thickness = gr.Slider(0, 99, value=20, step=1, label="Skin Thickness")
+        
+        with gr.Column():
+            insulin = gr.Slider(0, 846, value=80, step=1, label="Insulin")
+            bmi = gr.Slider(0.0, 67.1, value=25.0, step=0.1, label="BMI")
+            diabetes_pedigree = gr.Slider(0.078, 2.42, value=0.5, step=0.001, label="Diabetes Pedigree")
+            age = gr.Slider(21, 81, value=30, step=1, label="Age")
+    
+    diagnosis = gr.Radio(["Yes", "No"], label="Your Diagnosis", info="Do you think this patient has diabetes?")
+    submit_btn = gr.Button("Submit Diagnosis", variant="primary")
+    
+    with gr.Row():
+        result_box = gr.Textbox(label="Game Result", lines=5)
+        tips_box = gr.Textbox(label="Health Tips & Information", lines=5)
+    
+    with gr.Row():
+        radar_plot = gr.Plot(label="Patient Risk Factors")
+        importance_plot = gr.Plot(label="Feature Importance")
+    
+    submit_btn.click(
+        fn=game.predict_and_play,
+        inputs=[pregnancies, glucose, blood_pressure, skin_thickness,
+                insulin, bmi, diabetes_pedigree, age, diagnosis],
+        outputs=[result_box, radar_plot, importance_plot, tips_box]
+    )
+    
+    gr.Markdown("""
+    ## 📚 About the Features
+    
+    - **Pregnancies**: Number of times pregnant
+    - **Glucose**: Plasma glucose concentration (2 hours in an oral glucose tolerance test)
+    - **Blood Pressure**: Diastolic blood pressure (mm Hg)
+    - **Skin Thickness**: Triceps skin fold thickness (mm)
+    - **Insulin**: 2-Hour serum insulin (mu U/ml)
+    - **BMI**: Body mass index (weight in kg/(height in m)²)
+    - **Diabetes Pedigree**: A function scoring likelihood of diabetes based on family history
+    - **Age**: Age in years
+    """)
+
+if __name__ == "__main__":
+    interface.launch()