Update funcs.py

funcs.py

@@ -15,8 +15,6 @@ from ai71 import AI71
 if torch.cuda.is_available():
     model = model.to('cuda')
 
-# dials_embeddings = pd.read_pickle('dials_embeddings.pkl')
-# dials_embeddings = pd.read_pickle('https://huggingface.co/datasets/vsrinivas/CBT_dialogue_embed_ds/resolve/main/dials_embeddings.pkl')
 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()
@@ -27,6 +25,8 @@ classifier = pipeline("zero-shot-classification", model ='facebook/bart-large-mn
 AI71_BASE_URL = "https://api.ai71.ai/v1/"
 AI71_API_KEY = os.getenv('AI71_API_KEY')
 
+session_conversation=[]
+
 # Detect emotions from patient dialogues
 def detect_emotions(text):
   emotion = classifier(text, candidate_labels=emotion_group_labels, batch_size=16)
@@ -72,8 +72,13 @@ def generate_triggers_img(items):
     triggers_img = Image.open('triggeres.png')
     return triggers_img
 
-# Generate therapist responses and patient triggers
-def get_doc_response_emotions(user_message, therapy_session_conversation):
+
+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)
@@ -92,75 +97,65 @@ def get_doc_response_emotions(user_message, therapy_session_conversation):
 
     therapy_session_conversation.append(["User: "+user_message, "Therapist: "+doc_response])
 
-    session_conversation.extend(["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+1]['Doctor']}\n\n")
     print(f"Therapist's response: {dials_embeddings.iloc[top_match_index]['Doctor']}\n\n")
 
     return '', therapy_session_conversation, emotions_msg
 
-# Generate summarization and recommendations for teh session
-def summarize_and_recommend():
-  session_time = str(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
-  session_conversation_processed = 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)
-
-  AI71_BASE_URL = "https://api.ai71.ai/v1/"
-
-  client = OpenAI(
-      api_key=AI71_API_KEY,
-      base_url=AI71_BASE_URL,
-  )
-
-  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
-          # print("Chunk summary:", summary, sep="", end="", flush=True)
-          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
-          # print("Chunk recommendation:", rec, sep="", end="", flush=True)
-          full_recommendations += rec
-  full_recommendations = full_recommendations.replace('User:', '').strip()
-  print("\n")
-  print("Full recommendations:", full_recommendations)
-  session_conversation=[]
-  return full_summary, full_recommendations
+  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)
+    session_conversation=[]
+    return full_summary, full_recommendations