Update funcs.py

funcs.py

@@ -11,7 +11,6 @@ import os
 from datetime import datetime
 from openai import OpenAI
 from ai71 import AI71
-import gradio as gr
 
 if torch.cuda.is_available():
     model = model.to('cuda')
@@ -23,7 +22,6 @@ with open ('emotion_group_labels.txt') as file:
 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
@@ -85,13 +83,10 @@ def get_doc_response_emotions(user_message, therapy_session_conversation):
       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")
@@ -99,17 +94,10 @@ def get_doc_response_emotions(user_message, therapy_session_conversation):
     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 = [item[0] for item in therapy_session_conversation]
-    print(type(session_conversation), session_conversation)
     session_conversation = [x for x in session_conversation if x is not None]
-
-    # 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.insert(0, "Session_time: "+session_time)
     
@@ -161,95 +149,4 @@ def summarize_and_recommend(therapy_session_conversation):
     print("\n")
     print("Full recommendations:", full_recommendations)
     chatbox=[]
-    return full_summary, full_recommendations, chatbox
-
-
-  
-
-
-# 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
+    return full_summary, full_recommendations, chatbox