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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# --- Configuration ---
MODEL_FILE = 'pmad_prediction_model.joblib'
# These MUST match the order from your data_model_generator.py
MODEL_FEATURES = [
'PPD_History', 'Anxiety_History', 'Birth_Trauma', 'Support_Score',
'Sentiment_Score', 'Keyword_Guilt_Freq', 'Keyword_Worry_Freq',
'Keyword_Intrusive_Freq', 'Keyword_Delusion_Freq',
'Avg_Sleep_Duration_hrs', 'Sleep_Fragmentation_Score',
'Avg_Heart_Rate_Variability', 'Activity_Steps_Avg'
]
# --- Load Model ---
try:
model = joblib.load(MODEL_FILE)
print(f"✅ Model '{MODEL_FILE}' loaded successfully.")
except FileNotFoundError:
print(f"❌ ERROR: Model file '{MODEL_FILE}' not found. Did you upload it?")
model = None
except Exception as e:
print(f"❌ ERROR: Could not load model: {e}")
model = None
# --- Init Flask App ---
app = Flask(__name__)
# --- Helper function to analyze the journal ---
def analyze_journal(journal_text):
text = journal_text.lower()
analysis = {
'Sentiment_Score': 0.0, # Neutral
'Keyword_Guilt_Freq': text.count('guilt') + text.count('failing') + text.count('bad mom'),
'Keyword_Worry_Freq': text.count('worry') + text.count('anxious') + text.count('scared'),
'Keyword_Intrusive_Freq': text.count('intrusive') + text.count('harm'),
'Keyword_Delusion_Freq': text.count('delusion') + text.count('not real')
}
# Simple sentiment
negative_words = analysis['Keyword_Guilt_Freq'] + analysis['Keyword_Worry_Freq']
if negative_words > 2:
analysis['Sentiment_Score'] = -0.7
elif negative_words > 0:
analysis['Sentiment_Score'] = -0.3
if text.count('happy') > 0 or text.count('great') > 0:
analysis['Sentiment_Score'] = 0.5
return analysis
@app.route("/")
def home():
return "Maia PMAD Model API (Complex) is running."
@app.route("/predict", methods=['POST'])
def predict():
if model is None:
return jsonify({"error": "Model is not loaded"}), 500
try:
# 1. Get data from Flutter app
data = request.json
print(f"Received data: {data}")
# 2. Get the features we *can* get from the app
# We use the app's 'mood' (1-5) to help set sentiment
app_mood = data.get('mood', 3)
app_sleep = data.get('sleep', 7.0)
app_journal = data.get('journal', '')
# 3. Analyze the journal to get keyword/sentiment features
journal_analysis = analyze_journal(app_journal)
# Override sentiment if mood is very low
if app_mood <= 2 and journal_analysis['Sentiment_Score'] > -0.5:
journal_analysis['Sentiment_Score'] = -0.5
# 4. **INFER MISSING FEATURES**
# We create a full 13-feature dictionary, setting defaults
# for features the app doesn't collect.
input_data = {
# --- Inferred from Journal ---
'Sentiment_Score': journal_analysis['Sentiment_Score'],
'Keyword_Guilt_Freq': journal_analysis['Keyword_Guilt_Freq'],
'Keyword_Worry_Freq': journal_analysis['Keyword_Worry_Freq'],
'Keyword_Intrusive_Freq': journal_analysis['Keyword_Intrusive_Freq'],
'Keyword_Delusion_Freq': journal_analysis['Keyword_Delusion_Freq'],
# --- From App ---
'Avg_Sleep_Duration_hrs': app_sleep,
# --- Faked Defaults (Set to "average" or "neutral") ---
'PPD_History': 0, # Assume no history
'Anxiety_History': 0, # Assume no history
'Birth_Trauma': 0, # Assume no trauma
'Support_Score': 3.0, # Assume average support
'Sleep_Fragmentation_Score': 0.5, # Assume average
'Avg_Heart_Rate_Variability': 50.0, # Assume average
'Activity_Steps_Avg': 4000 # Assume average
}
# 5. Create the DataFrame in the *exact* order
df = pd.DataFrame([input_data], columns=MODEL_FEATURES)
print(f"Model input vector:\n{df.to_string()}")
# 6. Make prediction
prediction_proba = model.predict_proba(df)
# 7. Format the response
class_map = {0: 'Healthy', 1: 'PPD', 2: 'PPA', 3: 'P-OCD', 4: 'Severe/PPP/PTSD'}
probabilities = prediction_proba[0]
output = {
class_map[i]: prob for i, prob in enumerate(probabilities)
}
print(f"Prediction: {output}")
return jsonify(output)
except Exception as e:
print(f"❌ Error during prediction: {e}")
return jsonify({"error": str(e)}), 400
if __name__ == '__main__':
# HF Spaces uses port 7860
app.run(host='0.0.0.0', port=7860) |