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import gradio as gr
import os
import time
import openai
import pandas as pd
openai.api_key = ""
openai_api_key_textbox = ""
model = None
tokenizer = None
generator = None
csv_name = "disease_database_mini.csv"
df = pd.read_csv(csv_name)
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.replace(",","").split(" ")
print(keyword_list)
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>4:
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
with gr.Blocks() as demo:
gr.Markdown("# Autonomous ChatDoctor (openai version), based on disease database knowledge")
gr.Markdown("## Example: If I have frontal headache, fever, and painful sinuses, what disease should I have, and what medical test should I take?")
openai_api_key_textbox = gr.Textbox(placeholder="Paste your OpenAI API key (sk-...) and hit Enter",
show_label=False, lines=1, type='password')
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.Button("Clear")
Initialization = gr.Button("Initialization")
def restart(history):
invitation = "ChatDoctor: "
human_invitation = "Patient: "
return [[None,invitation+" I am ChatDoctor, what medical questions do you have?"]]
def user(user_message, history):
invitation = "ChatDoctor: "
human_invitation = "Patient: "
return "", history +[[human_invitation+user_message, None]]
def bot(history):
invitation = "ChatDoctor: "
human_invitation = "Patient: "
print(history)
print(openai_api_key_textbox)
openai.api_key = openai_api_key_textbox
question = ""
for each_ques in history:
question = question+ each_ques[0].replace("Patient: ","")+" \n"
response = csv_prompter(question,csv_name)
response = invitation+ response
history[-1][1] = response
return history
msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
bot, chatbot, chatbot
)
clear.click(lambda: None, None, chatbot, queue=False).then(restart, chatbot, chatbot)
Initialization.click(lambda: None, None, chatbot, queue=False).then(restart, chatbot, chatbot)
gr.Dataframe(df)
if __name__ == "__main__":
demo.launch()
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