File size: 3,709 Bytes
5e8fbc5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
from flask import Flask, render_template, request, jsonify, session
import tensorflow as tf
from transformers import T5Tokenizer, TFT5ForConditionalGeneration
import joblib
import pandas as pd
import datetime
app = Flask(__name__)
app.secret_key = 'hassaanik' # Necessary for session management
# Load models and tokenizers
counseling_greeting_model = TFT5ForConditionalGeneration.from_pretrained('./models/counseling_greeting_model/saved_model')
counseling_greeting_tokenizer = T5Tokenizer.from_pretrained('./models/counseling_greeting_model/tokenizer')
med_info_model = TFT5ForConditionalGeneration.from_pretrained('./models/medication_info_model/saved_model')
med_info_tokenizer = T5Tokenizer.from_pretrained('./models/medication_info_model/tokenizer')
knn_model = joblib.load('./models/medication_classification_model/knn_model.pkl')
label_encoders = joblib.load('./models/medication_classification_model/label_encoders.pkl')
age_scaler = joblib.load('./models/medication_classification_model/age_scaler.pkl')
medication_encoder = joblib.load('./models/medication_classification_model/medication_encoder.pkl')
# Existing model loading code...
@app.route('/')
def index():
session.clear() # Clear session when accessing the homepage
return render_template('index.html')
@app.route('/reset_chat', methods=['POST'])
def reset_chat():
session.clear()
return jsonify({'status': 'Chat reset'})
def generate_response(model, tokenizer, input_text, session_key):
# Prepare input for model
encoding = tokenizer(input_text, max_length=500, padding='max_length', truncation=True, return_tensors='tf')
input_ids = encoding['input_ids']
attention_mask = encoding['attention_mask']
# Generate response
outputs = model.generate(input_ids=input_ids, attention_mask=attention_mask, max_length=512, num_beams=5, early_stopping=True)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Store in session
if session_key not in session:
session[session_key] = []
session[session_key].append({'user': input_text, 'bot': response})
return response
@app.route('/counseling_greeting', methods=['POST'])
def counseling_greeting():
data = request.get_json()
prompt = data['prompt']
response = generate_response(counseling_greeting_model, counseling_greeting_tokenizer, f"question: {prompt}", 'counseling_greeting')
return jsonify({'response': response, 'conversation': session['counseling_greeting']})
@app.route('/medication_info', methods=['POST'])
def medication_info():
data = request.get_json()
question = data['question']
response = generate_response(med_info_model, med_info_tokenizer, f"question: {question}", 'medication_info')
return jsonify({'response': response, 'conversation': session['medication_info']})
@app.route('/classify_medication', methods=['POST'])
def classify_medication():
data = pd.DataFrame([request.get_json()])
for column in ['Gender', 'Blood Type', 'Medical Condition', 'Test Results']:
data[column] = label_encoders[column].transform(data[column])
data['Age'] = age_scaler.transform(data[['Age']])
predictions = knn_model.predict(data)
predicted_medications = medication_encoder.inverse_transform(predictions)
if 'classify_medication' not in session:
session['classify_medication'] = []
session['classify_medication'].append({'user': data.to_dict(), 'bot': predicted_medications[0]})
return jsonify({'medication': predicted_medications[0], 'conversation': session['classify_medication']})
if __name__ == '__main__':
app.run(debug=True)
|