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main.py.py

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+# -*- coding: utf-8 -*-
+"""TestAPI.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1WToaz7kQoFpI0_M8j6uWPigBrKlkL4ml
+"""
+
+
+
+from transformers import AutoTokenizer,AutoModelForCausalLM
+import os
+os.environ["CUDA_VISIBLE_DEVICES"]="0"
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from typing import List
+
+
+from peft import PeftModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from typing import Any, List, Mapping, Optional
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from langchain.callbacks.manager import CallbackManagerForLLMRun
+from langchain.llms.base import LLM
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+import mysql.connector
+import re
+from datetime import datetime
+
+
+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
+
+
+model_name = "Open-Orca/OpenOrca-Platypus2-13B"
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name, trust_remote_code=True)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+    load_in_8bit = True,
+    device_map = "auto",
+)
+
+
+
+model = PeftModel.from_pretrained(model, "teslalord/open-orca-platypus-2-medical")
+
+model = model.merge_and_unload()
+
+
+
+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()
+

requirements.txt.txt

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+fastapi
+nest-asyncio
+pyngrok
+uvicorn
+langchain
+mysql-connector-python
+transformers
+accelerate
+evaluate
+datasets
+peft
+torch
+huggingface_hub
+bitsandbytes
+loralib
+git+https://github.com/huggingface/transformers.git@main
+git+https://github.com/huggingface/peft.git