funcs.py

import torch
import numpy as np
import io
import matplotlib.pyplot as plt
import pandas as pd
from sentence_transformers import SentenceTransformer
from transformers import pipeline
from datetime import datetime
from PIL import Image
import os
from datetime import datetime
from openai import OpenAI
from ai71 import AI71

if torch.cuda.is_available():
    model = model.to('cuda')

dials_embeddings = pd.read_pickle('https://huggingface.co/datasets/vsrinivas/CBT_dialogue_embed_ds/resolve/main/kaggle_therapy_embeddings.pkl')
with open ('emotion_group_labels.txt') as file:
    emotion_group_labels = file.read().splitlines()

embed_model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
classifier = pipeline("zero-shot-classification", model ='facebook/bart-large-mnli')

AI71_BASE_URL = "https://api.ai71.ai/v1/"
AI71_API_KEY = os.getenv('AI71_API_KEY')

# Detect emotions from patient dialogues
def detect_emotions(text):
  emotion = classifier(text, candidate_labels=emotion_group_labels, batch_size=16)
  top_5_scores = [i/sum(emotion['scores'][:5]) for i in emotion['scores'][:5]]
  top_5_emotions = emotion['labels'][:5]
  emotion_set = {l: "{:.2%}".format(s) for l, s in zip(top_5_emotions, top_5_scores)}
  return emotion_set

# Measure cosine similarity between a pair of vectors
def cosine_distance(vec1,vec2):
  cosine = (np.dot(vec1, vec2)/(np.linalg.norm(vec1)*np.linalg.norm(vec2)))
  return cosine

# Generate an image of trigger emotions
def generate_triggers_img(items):
    labels = list(items.keys())
    values = [float(v.strip('%')) for v in items.values()]  # Convert to float for plotting

    new_items = {k:v for k, v in zip(labels, values)}
    new_items = dict(sorted(new_items.items(), key=lambda item: item[1]))
    labels = list(new_items.keys())
    values = list(new_items.values())

    fig, ax = plt.subplots(figsize=(10, 6))
    colors = plt.cm.viridis(np.linspace(0, 1, len(labels)))

    bars = ax.barh(labels, values, color=colors)

    for spine in ax.spines.values():
        spine.set_visible(False)

    ax.tick_params(axis='y', labelsize=18)
    ax.xaxis.set_visible(False)
    ax.yaxis.set_ticks_position('none')

    for bar in bars:
        width = bar.get_width()
        ax.text(width, bar.get_y() + bar.get_height()/2, f'{width:.2f}%',
                ha='left', va='center', fontweight='bold', fontsize=18)

    plt.tight_layout()
    plt.savefig('triggeres.png')
    triggers_img = Image.open('triggeres.png')
    return triggers_img

def get_doc_response_emotions(user_message, therapy_session_conversation):
    user_messages = []
    user_messages.append(user_message)
    emotion_set = detect_emotions(user_message)
    print(emotion_set)

    emotions_msg = generate_triggers_img(emotion_set)
    user_embedding = embed_model.encode(user_message, device='cuda' if torch.cuda.is_available() else 'cpu')

    similarities =[]
    for v in dials_embeddings['embeddings']:
      similarities.append(cosine_distance(user_embedding,v))

    top_match_index = similarities.index(max(similarities))
    # doc_response = dials_embeddings.iloc[top_match_index+1]['Doctor']
    doc_response = dials_embeddings.iloc[top_match_index]['Doctor']

    therapy_session_conversation.append(["User: "+user_message, "Therapist: "+doc_response])

    # session_conversation.extend(["User: "+user_message, "Therapist: "+doc_response])

    print(f"User's message: {user_message}")
    print(f"RAG Matching message: {dials_embeddings.iloc[top_match_index]['Patient']}")
    print(f"Therapist's response: {dials_embeddings.iloc[top_match_index]['Doctor']}\n\n")

    return '', therapy_session_conversation, emotions_msg

def summarize_and_recommend(therapy_session_conversation):
    print("tcs:", therapy_session_conversation, type(therapy_session_conversation))

    session_time = str(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
    session_conversation_processed = [item[0] for item in therapy_session_conversation]
    print(type(session_conversation_processed), session_conversation_processed)
    # session_conversation_processed = [session_time] + therapy_session_conversation
    # session_conversation_processed = session_conversation.copy()
    # session_conversation_processed.insert(0, "Session_time: "+session_time)
    # session_conversation_processed ='\n'.join(session_conversation_processed)
    session_conversation_processed.insert(0, "Session_time: "+session_time)
    # session_conversation_processed ='\n'.join(therapy_session_conversation)
    print("session_conversation_processed:", session_conversation_processed)

    full_summary = ""
    for chunk in AI71(AI71_API_KEY).chat.completions.create(
        model="tiiuae/falcon-180b-chat",
        messages=[
            {"role": "system", "content": """You are an Expert Cognitive Behavioural Therapist and Precis writer.
            Summarize 'STRICTLY' the below user content <<<session_conversation_processed>>> 'ONLY' into useful, ethical, relevant and realistic phrases with a format
          Session Time:
          Summary of the patient messages: #in two to four sentences
          Summary of therapist messages: #in two to three sentences:
          Summary of the whole session: # in two to three sentences. Ensure the entire session summary strictly does not exceed 100 tokens."""},
            {"role": "user", "content": session_conversation_processed},
        ],
        stream=True,
    ):
        if chunk.choices[0].delta.content:
            summary = chunk.choices[0].delta.content
            full_summary += summary
    full_summary = full_summary.replace('User:', '').strip()
    print("\n")
    print("Full summary:", full_summary)

    full_recommendations = ""
    for chunk in AI71(AI71_API_KEY).chat.completions.create(
        model="tiiuae/falcon-180b-chat",
        messages=[
            {"role": "system", "content": """You are an expert Cognitive Behavioural Therapist.
            Based on 'STRICTLY' the full summary <<<full_summary>>> 'ONLY' provide clinically valid, useful, appropriate action plan for the Patient as a bullted list.
            The list shall contain both medical and non medical prescriptions, dos and donts. The format of response shall be in passive voice with proper tense.
          - The patient is referred to........ #in one sentence
          - The patient is advised to ........ #in one sentence
          - The patient is refrained from........ #in one sentence
          - It is suggested  that tha patient ........ #in one sentence
          - Scheduled a follow-up session with the patient........#in one sentence
            *Ensure the list contains NOT MORE THAN 7 points"""},
            {"role": "user", "content": full_summary},
        ],
        stream=True,
    ):
        if chunk.choices[0].delta.content:
            rec = chunk.choices[0].delta.content
            full_recommendations += rec
    full_recommendations = full_recommendations.replace('User:', '').strip()
    print("\n")
    print("Full recommendations:", full_recommendations)
    therapy_session_conversation=[]
    return full_summary, full_recommendations


  


# class process_session():
  # def __init__(self):
  #   self.session_conversation=[]

  # def get_doc_response_emotions(self, user_message, therapy_session_conversation):

  #   user_messages = []
  #   user_messages.append(user_message)
  #   emotion_set = detect_emotions(user_message)
  #   print(emotion_set)

  #   emotions_msg = generate_triggers_img(emotion_set)
  #   user_embedding = embed_model.encode(user_message, device='cuda' if torch.cuda.is_available() else 'cpu')

  #   similarities =[]
  #   for v in dials_embeddings['embeddings']:
  #     similarities.append(cosine_distance(user_embedding,v))

  #   top_match_index = similarities.index(max(similarities))
  #   # doc_response = dials_embeddings.iloc[top_match_index+1]['Doctor']
  #   doc_response = dials_embeddings.iloc[top_match_index]['Doctor']

  #   therapy_session_conversation.append(["User: "+user_message, "Therapist: "+doc_response])

  #   self.session_conversation.extend(["User: "+user_message, "Therapist: "+doc_response])

  #   print(f"User's message: {user_message}")
  #   print(f"RAG Matching message: {dials_embeddings.iloc[top_match_index]['Patient']}")
  #   print(f"Therapist's response: {dials_embeddings.iloc[top_match_index]['Doctor']}\n\n")

  #   return '', therapy_session_conversation, emotions_msg

  # def summarize_and_recommend(self):

  #   session_time = str(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
  #   session_conversation_processed = self.session_conversation.copy()
  #   session_conversation_processed.insert(0, "Session_time: "+session_time)
  #   session_conversation_processed ='\n'.join(session_conversation_processed)
  #   print("Session conversation:", session_conversation_processed)

  #   full_summary = ""
  #   for chunk in AI71(AI71_API_KEY).chat.completions.create(
  #       model="tiiuae/falcon-180b-chat",
  #       messages=[
  #           {"role": "system", "content": """You are an Expert Cognitive Behavioural Therapist and Precis writer.
  #           Summarize the below user content <<<session_conversation_processed>>> into useful, ethical, relevant and realistic phrases with a format
  #         Session Time:
  #         Summary of the patient messages: #in two to four sentences
  #         Summary of therapist messages: #in two to three sentences:
  #         Summary of the whole session: # in two to three sentences. Ensure the entire session summary strictly does not exceed 100 tokens."""},
  #           {"role": "user", "content": session_conversation_processed},
  #       ],
  #       stream=True,
  #   ):
  #       if chunk.choices[0].delta.content:
  #           summary = chunk.choices[0].delta.content
  #           full_summary += summary
  #   full_summary = full_summary.replace('User:', '').strip()
  #   print("\n")
  #   print("Full summary:", full_summary)

  #   full_recommendations = ""
  #   for chunk in AI71(AI71_API_KEY).chat.completions.create(
  #       model="tiiuae/falcon-180b-chat",
  #       messages=[
  #           {"role": "system", "content": """You are an expert Cognitive Behavioural Therapist.
  #           Based on the full summary <<<full_summary>>> provide clinically valid, useful, appropriate action plan for the Patient as a bullted list.
  #           The list shall contain both medical and non medical prescriptions, dos and donts. The format of response shall be in passive voice with proper tense.
  #         - The patient is referred to........ #in one sentence
  #         - The patient is advised to ........ #in one sentence
  #         - The patient is refrained from........ #in one sentence
  #         - It is suggested  that tha patient ........ #in one sentence
  #         - Scheduled a follow-up session with the patient........#in one sentence
  #           *Ensure the list contains NOT MORE THAN 7 points"""},
  #           {"role": "user", "content": full_summary},
  #       ],
  #       stream=True,
  #   ):
  #       if chunk.choices[0].delta.content:
  #           rec = chunk.choices[0].delta.content
  #           full_recommendations += rec
  #   full_recommendations = full_recommendations.replace('User:', '').strip()
  #   print("\n")
  #   print("Full recommendations:", full_recommendations)
  #   self.session_conversation=[]
  #   return full_summary, full_recommendations