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Tegaconsult commited on Feb 23

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1 Parent(s): 9a647e6

Update app.py

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app.py +10 -398

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-import os
-import numpy as np
-import onnxruntime as rt
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import HTMLResponse
-from fastapi.staticfiles import StaticFiles
-from pydantic import BaseModel
-from typing import Dict, Any
-from huggingface_hub import hf_hub_download
-from dotenv import load_dotenv
-load_dotenv()
-app = FastAPI(title="Digital Doctors Assistant ML API")
-# Model configurations
-MODELS = {
-    'risk_assessment': {
-        'filename': 'risk_assessment.onnx',
-        'features': ['age', 'bmi', 'systolic_bp', 'diastolic_bp',
-                   'chronic_conditions_count', 'severity_score'],
-        'output_classes': ['Low', 'Medium', 'High']
-    },
-    'treatment_outcome': {
-        'filename': 'treatment_outcome.onnx',
-        'features': ['patient_age', 'severity_score', 'compliance_rate',
-                   'medication_encoded', 'condition_encoded'],
-        'output_classes': ['No Success', 'Success']
-    }
-}
-# Load models on startup
-risk_session = None
-treatment_session = None
-@app.on_event("startup")
-async def load_models():
-    global risk_session, treatment_session
-    # Get token from environment (set as Space secret)
-    token = os.getenv("HUGGINGFACE_TOKEN")
-    # Download and load risk assessment model
-    risk_path = hf_hub_download(
-        repo_id="Tegaconsult/digital-doctors-assistant-ml",
-        filename="risk_assessment.onnx",
-        token=token
-    )
-    risk_session = rt.InferenceSession(risk_path)
-    # Download and load treatment outcome model
-    treatment_path = hf_hub_download(
-        repo_id="Tegaconsult/digital-doctors-assistant-ml",
-        filename="treatment_outcome.onnx",
-        token=token
-    )
-    treatment_session = rt.InferenceSession(treatment_path)
-    print("Models loaded successfully!")
-class RiskAssessmentRequest(BaseModel):
-    age: float
-    bmi: float
-    systolic_bp: float
-    diastolic_bp: float
-    chronic_conditions: str = ""
-    severity_score: float
-class TreatmentOutcomeRequest(BaseModel):
-    patient_age: float
-    severity_score: float
-    compliance_rate: float
-    medication: str
-    condition: str
-@app.get("/", response_class=HTMLResponse)
-def root():
-    html_content = """<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Digital Doctors Assistant ML</title>
-    <style>
-        * { margin: 0; padding: 0; box-sizing: border-box; }
-        body { font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); min-height: 100vh; padding: 20px; }
-        .container { max-width: 1200px; margin: 0 auto; }
-        h1 { color: white; text-align: center; margin-bottom: 30px; font-size: 2.5em; }
-        .cards { display: grid; grid-template-columns: repeat(auto-fit, minmax(500px, 1fr)); gap: 20px; }
-        .card { background: white; border-radius: 15px; padding: 30px; box-shadow: 0 10px 30px rgba(0,0,0,0.2); }
-        .card h2 { color: #667eea; margin-bottom: 20px; font-size: 1.8em; }
-        .form-group { margin-bottom: 15px; }
-        label { display: block; margin-bottom: 5px; color: #333; font-weight: 600; }
-        input, textarea { width: 100%; padding: 10px; border: 2px solid #e0e0e0; border-radius: 8px; font-size: 14px; transition: border 0.3s; }
-        input:focus, textarea:focus { outline: none; border-color: #667eea; }
-        button { width: 100%; padding: 12px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white; border: none; border-radius: 8px; font-size: 16px; font-weight: 600; cursor: pointer; transition: transform 0.2s; }
-        button:hover { transform: translateY(-2px); }
-        button:active { transform: translateY(0); }
-        .result { margin-top: 20px; padding: 20px; background: #f8f9fa; border-radius: 8px; border-left: 4px solid #667eea; }
-        .result h3 { color: #667eea; margin-bottom: 10px; }
-        .result-item { margin: 8px 0; color: #555; }
-        .result-item strong { color: #333; }
-        .error { background: #fee; border-left-color: #f44; }
-        .error h3 { color: #f44; }
-        .hidden { display: none; }
-        .risk-low { color: #28a745; font-weight: bold; }
-        .risk-medium { color: #ffc107; font-weight: bold; }
-        .risk-high { color: #dc3545; font-weight: bold; }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <h1>Digital Doctors Assistant ML</h1>
-        <div class="cards">
-            <div class="card">
-                <h2>Risk Assessment</h2>
-                <form id="riskForm">
-                    <div class="form-group">
-                        <label>Age</label>
-                        <input type="number" id="age" required min="0" max="120" value="45">
-                    </div>
-                    <div class="form-group">
-                        <label>BMI</label>
-                        <input type="number" id="bmi" required step="0.1" min="10" max="50" value="28.5">
-                    </div>
-                    <div class="form-group">
-                        <label>Systolic BP</label>
-                        <input type="number" id="systolic_bp" required min="70" max="200" value="140">
-                    </div>
-                    <div class="form-group">
-                        <label>Diastolic BP</label>
-                        <input type="number" id="diastolic_bp" required min="40" max="130" value="90">
-                    </div>
-                    <div class="form-group">
-                        <label>Chronic Conditions (comma-separated)</label>
-                        <input type="text" id="chronic_conditions" placeholder="e.g., diabetes,hypertension" value="diabetes,hypertension">
-                    </div>
-                    <div class="form-group">
-                        <label>Severity Score (0-10)</label>
-                        <input type="number" id="severity_score" required step="0.1" min="0" max="10" value="7.5">
-                    </div>
-                    <button type="submit">Predict Risk</button>
-                </form>
-                <div id="riskResult" class="result hidden"></div>
-            </div>
-            <div class="card">
-                <h2>Treatment Outcome</h2>
-                <form id="treatmentForm">
-                    <div class="form-group">
-                        <label>Patient Age</label>
-                        <input type="number" id="patient_age" required min="0" max="120" value="55">
-                    </div>
-                    <div class="form-group">
-                        <label>Severity Score (0-10)</label>
-                        <input type="number" id="treatment_severity" required step="0.1" min="0" max="10" value="6.5">
-                    </div>
-                    <div class="form-group">
-                        <label>Compliance Rate (0-1)</label>
-                        <input type="number" id="compliance_rate" required step="0.01" min="0" max="1" value="0.85">
-                    </div>
-                    <div class="form-group">
-                        <label>Medication</label>
-                        <input type="text" id="medication" required list="medications" value="Metformin">
-                        <datalist id="medications">
-                            <option value="Paracetamol">
-                            <option value="Ibuprofen">
-                            <option value="Amoxicillin">
-                            <option value="Ciprofloxacin">
-                            <option value="Metformin">
-                            <option value="Lisinopril">
-                            <option value="Amlodipine">
-                            <option value="Omeprazole">
-                        </datalist>
-                    </div>
-                    <div class="form-group">
-                        <label>Condition</label>
-                        <input type="text" id="condition" required list="conditions" value="Diabetes Type 2">
-                        <datalist id="conditions">
-                            <option value="Common Cold">
-                            <option value="Influenza">
-                            <option value="Pneumonia">
-                            <option value="Bronchitis">
-                            <option value="Hypertension">
-                            <option value="Diabetes Type 2">
-                            <option value="Migraine">
-                            <option value="Gastroenteritis">
-                        </datalist>
-                    </div>
-                    <button type="submit">Predict Outcome</button>
-                </form>
-                <div id="treatmentResult" class="result hidden"></div>
-            </div>
-        </div>
-    </div>
-    <script>
-        document.getElementById('riskForm').addEventListener('submit', async (e) => {
-            e.preventDefault();
-            const resultDiv = document.getElementById('riskResult');
-            const data = {
-                age: parseFloat(document.getElementById('age').value),
-                bmi: parseFloat(document.getElementById('bmi').value),
-                systolic_bp: parseFloat(document.getElementById('systolic_bp').value),
-                diastolic_bp: parseFloat(document.getElementById('diastolic_bp').value),
-                chronic_conditions: document.getElementById('chronic_conditions').value,
-                severity_score: parseFloat(document.getElementById('severity_score').value)
-            };
-            try {
-                const response = await fetch('/predict/risk', {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify(data)
-                });
-                const result = await response.json();
-                if (result.success) {
-                    const riskClass = result.prediction.toLowerCase().replace(' ', '-');
-                    resultDiv.className = 'result';
-                    resultDiv.innerHTML = `
-                        <h3>Prediction Results</h3>
-                        <div class="result-item"><strong>Risk Level:</strong> <span class="risk-${riskClass}">${result.prediction}</span></div>
-                        <div class="result-item"><strong>Confidence:</strong> ${(result.confidence * 100).toFixed(1)}%</div>
-                        ${result.probabilities ? `
-                            <div class="result-item"><strong>Probabilities:</strong></div>
-                            <div class="result-item">Low: ${(result.probabilities.Low * 100).toFixed(1)}%</div>
-                            <div class="result-item">Medium: ${(result.probabilities.Medium * 100).toFixed(1)}%</div>
-                            <div class="result-item">High: ${(result.probabilities.High * 100).toFixed(1)}%</div>
-                        ` : ''}
-                    `;
-                } else {
-                    throw new Error('Prediction failed');
-                }
-            } catch (error) {
-                resultDiv.className = 'result error';
-                resultDiv.innerHTML = `<h3>Error</h3><div class="result-item">${error.message}</div>`;
-            }
-            resultDiv.classList.remove('hidden');
-        });
-        document.getElementById('treatmentForm').addEventListener('submit', async (e) => {
-            e.preventDefault();
-            const resultDiv = document.getElementById('treatmentResult');
-            const data = {
-                patient_age: parseFloat(document.getElementById('patient_age').value),
-                severity_score: parseFloat(document.getElementById('treatment_severity').value),
-                compliance_rate: parseFloat(document.getElementById('compliance_rate').value),
-                medication: document.getElementById('medication').value,
-                condition: document.getElementById('condition').value
-            };
-            try {
-                const response = await fetch('/predict/treatment', {
-                    method: 'POST',
-                    headers: { 'Content-Type': 'application/json' },
-                    body: JSON.stringify(data)
-                });
-                const result = await response.json();
-                if (result.success) {
-                    resultDiv.className = 'result';
-                    resultDiv.innerHTML = `
-                        <h3>Prediction Results</h3>
-                        <div class="result-item"><strong>Outcome:</strong> ${result.prediction === 1 ? 'Success' : 'No Success'}</div>
-                        <div class="result-item"><strong>Success Probability:</strong> ${result.success_probability}%</div>
-                        <div class="result-item"><strong>Confidence:</strong> ${(result.confidence * 100).toFixed(1)}%</div>
-                        ${result.probabilities ? `
-                            <div class="result-item"><strong>Probabilities:</strong></div>
-                            <div class="result-item">Failure: ${(result.probabilities.failure * 100).toFixed(1)}%</div>
-                            <div class="result-item">Success: ${(result.probabilities.success * 100).toFixed(1)}%</div>
-                        ` : ''}
-                    `;
-                } else {
-                    throw new Error('Prediction failed');
-                }
-            } catch (error) {
-                resultDiv.className = 'result error';
-                resultDiv.innerHTML = `<h3>Error</h3><div class="result-item">${error.message}</div>`;
-            }
-            resultDiv.classList.remove('hidden');
-        });
-    </script>
-</body>
-</html>"""
-    return html_content
-@app.post("/predict/risk")
-def predict_risk(request: RiskAssessmentRequest):
-    """Predict patient risk level"""
-    try:
-        # Prepare input
-        chronic_count = len(request.chronic_conditions.split(',')) if request.chronic_conditions else 0
-        input_data = np.array([[
-            request.age,
-            request.bmi,
-            request.systolic_bp,
-            request.diastolic_bp,
-            chronic_count,
-            request.severity_score
-        ]], dtype=np.float32)
-        # Run inference
-        input_name = risk_session.get_inputs()[0].name
-        result = risk_session.run(None, {input_name: input_data})
-        # Parse results
-        prediction = result[0][0]
-        probabilities = result[1][0] if len(result) > 1 else None
-        output_classes = MODELS['risk_assessment']['output_classes']
-        if isinstance(prediction, (int, np.integer)):
-            prediction_label = output_classes[prediction]
-        else:
-            prediction_label = prediction
-        confidence = float(max(probabilities)) if probabilities is not None else 0.0
-        return {
-            'success': True,
-            'model': 'risk_assessment',
-            'prediction': prediction_label,
-            'confidence': confidence,
-            'probabilities': {
-                output_classes[i]: float(probabilities[i])
-                for i in range(len(output_classes))
-            } if probabilities is not None else None
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-@app.post("/predict/treatment")
-def predict_treatment(request: TreatmentOutcomeRequest):
-    """Predict treatment outcome"""
-    try:
-        # Encode categorical variables
-        medication_mapping = {
-            'Paracetamol': 0, 'Ibuprofen': 1, 'Amoxicillin': 2, 'Ciprofloxacin': 3,
-            'Metformin': 4, 'Lisinopril': 5, 'Amlodipine': 6, 'Omeprazole': 7
-        }
-        condition_mapping = {
-            'Common Cold': 0, 'Influenza': 1, 'Pneumonia': 2, 'Bronchitis': 3,
-            'Hypertension': 4, 'Diabetes Type 2': 5, 'Migraine': 6, 'Gastroenteritis': 7
-        }
-        # Prepare input
-        input_data = np.array([[
-            request.patient_age,
-            request.severity_score,
-            request.compliance_rate,
-            medication_mapping.get(request.medication, 0),
-            condition_mapping.get(request.condition, 0)
-        ]], dtype=np.float32)
-        # Run inference
-        input_name = treatment_session.get_inputs()[0].name
-        result = treatment_session.run(None, {input_name: input_data})
-        # Parse results
-        prediction = result[0][0]
-        probabilities = result[1][0] if len(result) > 1 else None
-        success_probability = float(probabilities[1]) if probabilities is not None else 0.5
-        return {
-            'success': True,
-            'model': 'treatment_outcome',
-            'prediction': int(prediction),
-            'success_probability': round(success_probability * 100, 1),
-            'confidence': float(max(probabilities)) if probabilities is not None else 0.0,
-            'probabilities': {
-                'failure': float(probabilities[0]),
-                'success': float(probabilities[1])
-            } if probabilities is not None else None
-        }
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-@app.get("/health")
-def health_check():
-    return {
-        "status": "healthy",
-        "models_loaded": {
-            "risk_assessment": risk_session is not None,
-            "treatment_outcome": treatment_session is not None
-        }
-    }

+"""
+Alternative entry point for the application.
+This file can be used instead of ml.py if needed.
+"""
+from ml import app
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)