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import os
import sys
# Add the project root directory to sys.path
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../"))
sys.path.append(project_root)
import google.generativeai as genai
from flask import Flask, request, jsonify, render_template
from src.scripts.data_preprocessing import DataPreprocessor
from src.scripts.prediction import DiabetesPrediction
class HealthRecommendations:
def __init__(self, api_key):
# Configure Google GenAI
genai.configure(api_key=api_key)
# For this example, we'll use the gemini-pro model
self.model = genai.GenerativeModel('gemini-pro')
def get_recommendations(self, patient_data, prediction):
# Create a prompt for the model
prompt = f"""
Given the following patient data:
- Glucose level: {patient_data['Glucose']}
- Blood Pressure: {patient_data['BloodPressure']}
- BMI: {patient_data['BMI']}
- Age: {patient_data['Age']}
- Diabetes Prediction: {'Positive' if prediction == 1 else 'Negative'}
Please provide specific health recommendations for this patient considering their metrics
and diabetes risk status. Focus on diet, exercise, and lifestyle changes.
"""
# Generate response using Google GenAI
response = self.model.generate_content(prompt)
# Extract and return the recommendations
return response.text
app = Flask(__name__, template_folder='src/templates')
# Initialize components
predictor = DiabetesPrediction()
health_advisor = HealthRecommendations(api_key=os.getenv('AIzaSyBMh7bQCD1tf_9w7C04zNoJocEtHg9KLjI')) # Changed to use Google API key
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
try:
# Get data from request
data = request.json
features = [
float(data['pregnancies']),
float(data['glucose']),
float(data['bloodPressure']),
float(data['skinThickness']),
float(data['insulin']),
float(data['bmi']),
float(data['diabetesPedigree']),
float(data['age'])
]
# Make prediction
prediction_result = predictor.predict(features)
# Get health recommendations
recommendations = health_advisor.get_recommendations(
patient_data={
'Glucose': data['glucose'],
'BloodPressure': data['bloodPressure'],
'BMI': data['bmi'],
'Age': data['age']
},
prediction=prediction_result
)
return jsonify({
'prediction': prediction_result,
'recommendations': recommendations
})
except Exception as e:
return jsonify({'error': str(e)}), 400
if __name__ == '__main__':
app.run(debug=True) |