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Autonomous_ChatGPT_API/chat_openai.py

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+import openai
+import pandas as pd
+
+
+
+
+openai.api_key = ""
+
+
+
+
+def csv_prompter(question,csv_name):
+
+
+ fulltext = "A question is provided below. Given the question, extract " + \
+ "keywords from the text. Focus on extracting the keywords that we can use " + \
+ "to best lookup answers to the question. \n" + \
+ "---------------------\n" + \
+ "{}\n".format(question) + \
+ "---------------------\n" + \
+ "Provide keywords in the following comma-separated format.\nKeywords: "
+
+ messages = [
+ {"role": "system", "content": ""},
+ ]
+ messages.append(
+ {"role": "user", "content": f"{fulltext}"}
+ )
+ rsp = openai.ChatCompletion.create(
+ model="gpt-3.5-turbo",
+ messages=messages
+ )
+ keyword_list = rsp.get("choices")[0]["message"]["content"]
+ keyword_list = keyword_list.split(", ")
+
+ print(keyword_list)
+ df = pd.read_csv(csv_name)
+ divided_text = []
+ csvdata = df.to_dict('records')
+ step_length = 15
+ for csv_item in range(0,len(csvdata),step_length):
+ csv_text = str(csvdata[csv_item:csv_item+step_length]).replace("}, {", "\n\n").replace("\"", "")#.replace("[", "").replace("]", "")
+ divided_text.append(csv_text)
+
+ answer_llm = ""
+
+ score_textlist = [0] * len(divided_text)
+
+ for i, chunk in enumerate(divided_text):
+ for t, keyw in enumerate(keyword_list):
+ if keyw.lower() in chunk.lower():
+ score_textlist[i] = score_textlist[i] + 1
+
+ answer_list = []
+ divided_text = [item for _, item in sorted(zip(score_textlist, divided_text), reverse=True)]
+
+ for i, chunk in enumerate(divided_text):
+
+ if i>5:
+ continue
+
+ fulltext = "{}".format(chunk) + \
+ "\n---------------------\n" + \
+ "Based on the Table above and not prior knowledge, " + \
+ "Select the Table Entries that will help to answer the question: {}\n Output in the format of \" Disease: <>; Symptom: <>; Medical Test: <>; Medications: <>;\". If there is no useful form entries, output: 'No Entry'".format(question)
+
+ print(fulltext)
+ messages = [
+ {"role": "system", "content": ""},
+ ]
+ messages.append(
+ {"role": "user", "content": f"{fulltext}"}
+ )
+ rsp = openai.ChatCompletion.create(
+ model="gpt-3.5-turbo",
+ messages=messages
+ )
+ answer_llm = rsp.get("choices")[0]["message"]["content"]
+
+ print("\nAnswer: " + answer_llm)
+ print()
+ if not "No Entry" in answer_llm:
+ answer_list.append(answer_llm)
+
+
+
+ fulltext = "The original question is as follows: {}\n".format(question) + \
+ "Based on this Table:\n" + \
+ "------------\n" + \
+ "{}\n".format(str("\n\n".join(answer_list))) + \
+ "------------\n" + \
+ "Answer: "
+ print(fulltext)
+ messages = [
+ {"role": "system", "content": ""},
+ ]
+ messages.append(
+ {"role": "user", "content": f"{fulltext}"}
+ )
+ rsp = openai.ChatCompletion.create(
+ model="gpt-3.5-turbo",
+ messages=messages
+ )
+ answer_llm = rsp.get("choices")[0]["message"]["content"]
+
+ print("\nFinal Answer: " + answer_llm)
+ print()
+
+ return answer_llm
+
+
+
+question = "If I have frontal headache, fever, and painful sinuses, what disease should I have, and what medical test should I take?"
+csv_name = "disease_database_mini.csv"
+csv_prompter(question,csv_name)