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# utils/helper_functions.py
import time
import json
import csv
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
# Labeling logic
keywords_to_labels = {
'advice': ['try', 'should', 'suggest', 'recommend'],
'validation': ['understand', 'feel', 'valid', 'normal'],
'information': ['cause', 'often', 'disorder', 'symptom'],
'question': ['how', 'what', 'why', 'have you']
}
def auto_label_response(response):
response = response.lower()
for label, keywords in keywords_to_labels.items():
if any(word in response for word in keywords):
return label
return 'information'
def build_prompt(user_input, response_type):
prompts = {
"advice": f"A patient said: \"{user_input}\". What advice should a mental health counselor give to support them?",
"validation": f"A patient said: \"{user_input}\". How can a counselor validate and empathize with their emotions?",
"information": f"A patient said: \"{user_input}\". Explain what might be happening from a mental health perspective.",
"question": f"A patient said: \"{user_input}\". What thoughtful follow-up questions should a counselor ask?"
}
return prompts.get(response_type, prompts["information"])
def predict_response_type(user_input, model, vectorizer, label_encoder):
vec = vectorizer.transform([user_input])
pred = model.predict(vec)
proba = model.predict_proba(vec).max()
label = label_encoder.inverse_transform(pred)[0]
return label, proba
def generate_llm_response(prompt, llm):
start = time.time()
result = llm(prompt, max_tokens=300, temperature=0.7)
end = time.time()
elapsed = round(end - start, 1)
return result['choices'][0]['text'].strip(), elapsed
def trim_memory(history, max_turns=6):
return history[-max_turns * 2:]
def save_conversation(history):
timestamp = time.strftime("%Y%m%d-%H%M%S")
file_name = f"chat_log_{timestamp}.csv"
with open(file_name, "w", newline='') as f:
writer = csv.writer(f)
writer.writerow(["Role", "Content", "Intent", "Confidence"])
for turn in history:
writer.writerow([
turn.get("role", ""),
turn.get("content", ""),
turn.get("label", ""),
round(float(turn.get("confidence", 0)) * 100)
])
print(f"Saved to {file_name}")
return file_name
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