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 <<>> '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 <<>> '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 <<>> 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 <<>> 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