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compute_health_score.py

@@ -1,7 +1,7 @@
-import openai, os
-import pandas as pd
+import openai
+import os
 import re
-from Hopfield import retrieval_info
+import csv
 from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
 from llama_index import Prompt
 from llama_index import StorageContext, load_index_from_storage
@@ -10,20 +10,16 @@
     HumanMessagePromptTemplate,
     SystemMessagePromptTemplate,
 )
-import csv
-
-
-
 
-def answer_from_gpt(ques, context, work):
 
+def answer_from_gpt(ques, context):
     storage_context = StorageContext.from_defaults(persist_dir='./storage')
     index = load_index_from_storage(storage_context, index_id="index_health")
     list_score = []
 
     t = 0
     for i in ques:
-        my_context = context + work[t]
+        my_context = context
         QA_PROMPT = get_systemprompt_template(my_context)
         query_engine = index.as_query_engine(text_qa_template=QA_PROMPT)
         response = query_engine.query(i)
@@ -36,23 +32,21 @@ def answer_from_gpt(ques, context, work):
     return list_score
 
 
-
 def get_systemprompt_template(exist_context):
-
     chat_text_qa_msgs = [
         SystemMessagePromptTemplate.from_template(
             exist_context
         ),
         HumanMessagePromptTemplate.from_template(
-        "Give the answer in jason format with only one number between 0 and 1 that is: 'score'\n"
-        "The score number must be an decimals\n"
-        "This is the rule of answer: 0-0.2 is mild or none, 0.3-0.6 is moderate, and above 0.7 is severe.\n"
-        "This is a patient‘s medical record. Context information in below\n"
-        "---------------------\n"
-        "{context_str}"
-        "Given the context information, you are a helpful health consultant "
-        "answer the question: {query_str}\n"
-    )
+            "Give the answer in jason format with only one number between 0 and 1 that is: 'score'\n"
+            "The score number must be an decimals\n"
+            "This is the rule of answer: 0-0.2 is mild or none, 0.3-0.6 is moderate, and above 0.7 is severe.\n"
+            "This is a patient‘s medical record. Context information in below\n"
+            "---------------------\n"
+            "{context_str}"
+            "Given the context information, you are a helpful health consultant "
+            "answer the question: {query_str}\n"
+        )
     ]
     chat_text_qa_msgs_lc = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
     text_qa_template = Prompt.from_langchain_prompt(chat_text_qa_msgs_lc)
@@ -79,13 +73,9 @@ def generate_question(path):
     question = []
     for i in my_feature_list:
         sentence = f"Does the person described in the case have {i} symptoms? Do you think it is serious?"
-        list_sentence = [sentence]
-        retrieval = retrieval_info(list_sentence, '/Users/jmy/Desktop/ai_for_health_final/', 1)
         question.append(sentence)
-        related_work.append(retrieval[0])
-        print(retrieval[0])
 
-    return question, related_work, my_feature_list
+    return question, my_feature_list
 
 
 def count_subfolders(folder_path):
@@ -96,65 +86,39 @@ def count_subfolders(folder_path):
         if root != folder_path:
             subfolder_count += 1
 
-    basepath = '/Users/jmy/Desktop/ai_for_health_final/dataset_folder/health_report_'
+    basepath = '/Users/chongzhang/PycharmProjects/ai_for_health_final/dataset_folder/health_report_'
     for i in range(subfolder_count):
-        path_rr = basepath+str({i})
+        path_rr = basepath + str({i})
         subfolder_paths.append(path_rr)
 
     return subfolder_count, subfolder_paths
 
 
-# def answer_from_gpt(ques,context):
-#     # rebuild storage context
-#     storage_context = StorageContext.from_defaults(persist_dir='./storage')
-#     # load index
-#     index = load_index_from_storage(storage_context, index_id="index_health")
-#
-#     list_score = []
-#     for i in ques:
-#         QA_PROMPT = get_systemprompt_template(context)
-#         query_engine = index.as_query_engine(text_qa_template=QA_PROMPT)
-#         response = query_engine.query(i)
-#         stt = str(response)
-#
-#         score = extract_score(stt)
-#         list_score.append(score)
-#         print(score)
-#
-#     return list_score
-
-
-def load_doc(folder_path,question,work):
-    print(len(work))
-    count,dict = count_subfolders(folder_path)
+def load_doc(folder_path, question):
+    count, dict = count_subfolders(folder_path)
     list_k = []
+    openai.api_key = "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"
     context = 'Here is some additional professional health knowledge that can help you better analyze the report'
-    for i in range(0,10000):
+    for i in range(0, 1):
         documents = SimpleDirectoryReader(dict[i]).load_data()
         index = GPTVectorStoreIndex.from_documents(documents)
         index.set_index_id("index_health")
         index.storage_context.persist('./storage')
         content = context
-        list = answer_from_gpt(question, content, work)
+        list = answer_from_gpt(question, content)
         list_k.append(list)
     return list_k
 
 
-
 if __name__ == '__main__':
 
-
     openai.api_key = os.environ.get("OPENAI_API_KEY")
-    path = '/Users/jmy/Desktop/ai_for_health_final/label and feature/input_feature.txt'
-    question, related_work, features_list = generate_question(path)
-    folder_path = '/Users/jmy/Desktop/ai_for_health_final/dataset_folder'
-    list = load_doc(folder_path, question, related_work)
+    path = '/Users/chongzhang/PycharmProjects/ai_for_health_final/label and feature/input_feature.txt'
+    question, features_list = generate_question(path)
+    folder_path = '/Users/chongzhang/PycharmProjects/ai_for_health_final/dataset_folder'
+    list = load_doc(folder_path, question)
 
-    with open('training/train.txt', 'w') as file:
-         for item in list:
-             file.write(''.join(str(item)) + '\n\n')
-
-    with open('training/combined7.csv', 'w', newline='') as file:
+    with open(f'training2/combined10.csv', 'w', newline='') as file:
         writer = csv.writer(file)
         # 首先写入特征行
         writer.writerow(features_list)
@@ -163,10 +127,3 @@ def load_doc(folder_path,question,work):
             writer.writerow(row)
 
     print("CSV file has been created successfully.")
-
-
-
-
-
-
-