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from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained(
"E-Hospital/open-orca-platypus-2-lora-medical",
trust_remote_code=True,
device_map = "auto",
)
tokenizer = AutoTokenizer.from_pretrained("Open-Orca/OpenOrca-Platypus2-13B", trust_remote_code=True)
def ask_bot(question):
input_ids = tokenizer.encode(question, return_tensors="pt").to('cuda')
with torch.no_grad():
output = model.generate(input_ids, max_length=500, num_return_sequences=1, do_sample=True, top_k=50)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
response = generated_text.split("->:")[-1]
return response
ask_bot("I have diabetes. What should I do?")
import mysql.connector
import re
from datetime import datetime
from typing import Any, List, Mapping, Optional
from langchain.memory import ConversationBufferMemory
from typing import Any, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from langchain.memory import ConversationSummaryBufferMemory
from langchain.memory import ConversationSummaryMemory
class CustomLLM(LLM):
n: int
# custom_model: llm # Replace with the actual type of your custom model
@property
def _llm_type(self) -> str:
return "custom"
def _call(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> str:
if stop is not None:
raise ValueError("stop kwargs are not permitted.")
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('cuda')
with torch.no_grad():
output = model.generate(input_ids, max_length=500, num_return_sequences=1, do_sample=True, top_k=50)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
response = generated_text.split("->:")[-1]
return response
@property
def _identifying_params(self) -> Mapping[str, Any]:
"""Get the identifying parameters."""
return {"n": self.n}
def ask_bot(question):
input_ids = tokenizer.encode(question, return_tensors="pt").to('cuda')
with torch.no_grad():
output = model.generate(input_ids, max_length=500, num_return_sequences=1, do_sample=True, top_k=50)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
response = generated_text.split("->:")[-1]
return response
class DbHandler():
def __init__(self):
self.db_con = mysql.connector.connect(
host="frwahxxknm9kwy6c.cbetxkdyhwsb.us-east-1.rds.amazonaws.com",
user="j6qbx3bgjysst4jr",
password="mcbsdk2s27ldf37t",
port=3306,
database="nkw2tiuvgv6ufu1z")
self.cursorObject = self.db_con.cursor()
def insert(self, fields, values):
try:
# Convert the lists to comma-separated strings
fields_str = ', '.join(fields)
values_str = ', '.join([f"'{v}'" for v in values]) # Wrap values in single quotes for SQL strings
# Construct the SQL query
query = f"INSERT INTO chatbot_conversation ({fields_str}) VALUES ({values_str})"
self.cursorObject.execute(query)
self.db_con.commit()
return True
except Exception as e:
print(e)
return False
def get_history(self, patient_id):
try:
query = f"SELECT * FROM chatbot_conversation WHERE patient_id = {patient_id} ORDER BY timestamp ASC;"
self.cursorObject.execute(query)
data = self.cursorObject.fetchall()
return data
except Exception as e:
print(e)
return None
def close_db(self):
self.db_con.close()
def get_conversation_history(db, patient_id):
conversations = db.get_history(patient_id)
if conversations:
return conversations[-1][5]
return ""
llm = CustomLLM(n=10)
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=['*'],
allow_credentials=True,
allow_methods=['*'],
allow_headers=['*'],
)
@app.get('/healthcheck')
async def root():
return {'status': 'running'}
@app.post('/{patient_id}')
def chatbot(patient_id, user_data: dict=None):
user_input = user_data["userObject"]["userInput"].get("message")
db = DbHandler()
try:
history = get_conversation_history(db, patient_id)
memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=200)
prompt = "You are now a medical chatbot, and I am a patient. I will describe my conditions and symptoms and you will give me medical suggestions"
if history:
human_input = prompt + "The following is the patient's previous conversation with you: " + history + "This is the current question: " + user_input + " ->:"
else:
human_input = prompt + user_input + " ->:"
human_text = user_input.replace("'", "")
response = llm._call(human_input)
response = response.replace("'", "")
memory.save_context({"input": user_input}, {"output": response})
summary = memory.load_memory_variables({})
ai_text = response.replace("'", "")
memory.save_context({"input": user_input}, {"output": ai_text})
summary = memory.load_memory_variables({})
db.insert(("patient_id", "patient_text", "ai_text", "timestamp", "summarized_text"), (patient_id, human_text, ai_text, str(datetime.now()), summary['history'].replace("'", "")))
db.close_db()
return {"response": response}
finally:
db.close_db()
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