main.py

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import StreamingResponse
from fastapi.responses import FileResponse, HTMLResponse
import os
import io
from fastapi.middleware.cors import CORSMiddleware
import google.generativeai as genai

import torch
from auto_gptq import AutoGPTQForCausalLM
from langchain import HuggingFacePipeline, PromptTemplate
from langchain.chains import RetrievalQA
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.vectorstores import FAISS
from transformers import AutoTokenizer, TextStreamer, pipeline

"""Patient Response : hi doctor \n\n"What brings you here today? Please provide me with some background information about your health so far." \nPatient Response : im arafath i have fever"""

DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"

GOOGLE_API_KEY = os.environ['GOOGLE_API_KEY']

genai.configure(api_key=GOOGLE_API_KEY)
gemini_model = genai.GenerativeModel('gemini-pro')

try: from pip._internal.operations import freeze
except ImportError: # pip < 10.0
    from pip.operations import freeze

pkgs = freeze.freeze()
for pkg in pkgs: print(pkg)

embeddings = HuggingFaceInstructEmbeddings(
    model_name="hkunlp/instructor-large", model_kwargs={"device": DEVICE}
)
new_db = FAISS.load_local("faiss_index", embeddings)

model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ"
model_basename = "model"


tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(
    model_name_or_path,
    revision="gptq-4bit-128g-actorder_True",
    model_basename=model_basename,
    use_safetensors=True,
    trust_remote_code=True,
    device=DEVICE,
    inject_fused_attention=False,
    quantize_config=None,
)

#default promts it will work when we don't set the our custom system propts
DEFAULT_SYSTEM_PROMPT = """
You are a helpful, respectful and honest assistant. give answer for any questions.
""".strip()


def generate_prompt(prompt: str, system_prompt: str = DEFAULT_SYSTEM_PROMPT) -> str:
    return f"""
[INST] <<SYS>>
{system_prompt}
<</SYS>>
{prompt} [/INST]
""".strip()

# setting the RAG pipeline
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
text_pipeline = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=4096,
    temperature=2,
    top_p=0.95,
    repetition_penalty=1.15,
    streamer=streamer,
)
global llm,llm2,llm3
llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 2})
llm2 = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 2})
llm3 = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 2})

# when the user query is not related to trained PDF data model will give the response from own knowledge 
SYSTEM_PROMPT = "give answer from external data's. don't use the provided context"

template = generate_prompt(
    """
{context}
Question: {question}
""",
    system_prompt=SYSTEM_PROMPT,
)
prompt = PromptTemplate(template=template, input_variables=["context", "question"])

global qa_chain,qa_chain_a,qa_chain_v

qa_chain_v = RetrievalQA.from_chain_type(
    llm=llm3,
    chain_type="stuff",
    retriever=store.as_retriever(search_kwargs={"k": 2}),
    return_source_documents=True,
    chain_type_kwargs={"prompt": prompt},
)

qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=new_db.as_retriever(search_kwargs={"k": 2}),
    return_source_documents=True,
    chain_type_kwargs={"prompt": prompt},
)

qa_chain_a = RetrievalQA.from_chain_type(
    llm=llm2,
    chain_type="stuff",
    retriever=new_db.as_retriever(search_kwargs={"k": 2}),
    return_source_documents=True,
    chain_type_kwargs={"prompt": prompt},
)

report_prompt_template = """
this is report format
Patient Name: [Insert name here]<br>
Age: [Insert age here]<br>
sex: [Insert  here]<br>
Chief Complaint: [insert here]<br>
History of Present Illness:[insert here]<br>
Past Medical History: [insert here]<br>
Medication List: [insert here]<br>
Social History: [insert here]<br>
Family History: [insert here]<br>
Review of Systems: [insert here]<br>
ICD Code: [insert here]
convert this bellow details into above format don't add any other details .don't use the provided pdfs data's.\n\n"""


# 4. prompt sets for ask some defined questions and its will guide the model correct way
final_question ={
    8:"Do you have a history of medical conditions, such as allergies, chronic illnesses, or previous surgeries? If so, please provide details.",
    9:"What medications are you currently taking, including supplements and vitamins?",
    10:"Can you please Describe Family medical history (particularly close relatives): Does anyone in your immediate family suffer from similar symptoms or health issues?",
    11:"Can you please Describe Social history: Marital status, occupation, living arrangements, education level, and support system.",
    12:"Could you describe your symptoms, and have you noticed any changes or discomfort related to your respiratory, cardiovascular, gastrointestinal, or other body systems?"
}

# 1 . basic first prompt for handled the llama in correct like a family physician
sys = "You are a general family physician.\n\n"

# 5 . prommpts for get the diagnosis with ICD code based on the conversation, its will handle unrelated questions also(not related to diagnosis)
end_sys_prompts = "\n\ngive correct treatment and most related diagnosis with ICD code don't ask any questions. if question is not related to provided data don't give answer from this provided data's"



def refresh_model():
        global llm,llm2,llm3
        llm = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 2})
        llm2 = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 2})
        llm3 = HuggingFacePipeline(pipeline=text_pipeline, model_kwargs={"temperature": 2})
    
        global qa_chain,qa_chain_a,qa_chain_v

        
        qa_chain_v = RetrievalQA.from_chain_type(
            llm=llm3,
            chain_type="stuff",
            retriever=store.as_retriever(search_kwargs={"k": 2}),
            return_source_documents=True,
            chain_type_kwargs={"prompt": prompt},
        )

        qa_chain = RetrievalQA.from_chain_type(
            llm=llm,
            chain_type="stuff",
            retriever=new_db.as_retriever(search_kwargs={"k": 2}),
            return_source_documents=True,
            chain_type_kwargs={"prompt": prompt},
        )
        
        qa_chain_a = RetrievalQA.from_chain_type(
            llm=llm2,
            chain_type="stuff",
            retriever=new_db.as_retriever(search_kwargs={"k": 2}),
            return_source_documents=True,
            chain_type_kwargs={"prompt": prompt},
        )
        print("model refreshed")

app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

@app.post("/llm_response/")
async def llm_response(chain,id,mode):
    id = int(id)
    global qa_chain,qa_chain_a,qa_chain_v
    refresh_model()

    def QA():
        print("\nopen QA mode running ========================================\n")

        try:
            print("\n google gemini===================\n")
            gemini_chat = gemini_model.start_chat(history=[])
            
            gemini_response = gemini_chat.send_message('give next small response for laste patient response like a doctor.  '+str(chain))
            return gemini_response.text
        except:
            print("\n llmmaa ===================\n")
            result_ex = qa_chain(sys+chain+"""\n\n\nalways give small and single response based on the patient 
                response. don't ask any question give simple response""")
            if "Patient:" in str(result_ex['result']) or "Patient response:" in str(result_ex['result']) or "Patient Response" in str(result_ex['result']):
                return str((str(result_ex['result']).split("\n\n")[-1]).split(":")[-1])
            else:
                try:
                   return str(result_ex['result']).split("\n\n")[1]
                except:
                   return str(result_ex['result']).split("\n\n")

    if str(mode)=="dirrect_QA" and id==3:
        diagnosis_and_treatment = qa_chain_v(sys+chain+end_sys_prompts)
        diagnosis_and_treatment = str(diagnosis_and_treatment['result'])
        print(diagnosis_and_treatment)
        print("dirrect answer")
        return diagnosis_and_treatment
    if str(mode)=="dirrect_QA" and id>3:
        return QA()
    
    if id<13:
            
        if id>=8:
            return final_question[id]
        else:
            if id<5:
                # 2 . prompmt control the natural way on question asking based on patient response,symptomps type
                question = qa_chain(sys+chain +"""\n\nask single small queston to get details based on the patient response,and don't ask 
                same question again, and don't provide treatment and diagnosis ask next small and short question , 
                always don't ask same question again and again , always only ask next single small question""")

                    
            else:
                # 3. prompt will guide the model to ask yes or no questions based on patient response,symptomps type
                question = qa_chain(sys+chain +"""\n\nask single small queston to get details based on the patient response,and don't ask 
                same question again, and don't provide treatment and diagnosis ask next small and short question with yes or no format , 
                always don't ask same question again and again , always only ask next single small question""")
            try:
                if "Patient:" in str(question['result']) or "Patient response:" in str(question['result']) or "Patient Response" in str(question['result']):
                    return str((str(question['result']).split("\n\n")[-1]).split(":")[-1])
                else:
                    return str(question['result']).split("\n\n")[1]

            except:
                if "Patient:" in str(question['result']) or "Patient response:" in str(question['result']) or "Patient Response" in str(question['result']):
                   return str(question['result']).split(":")[-1]
                else:
                   return str(question['result'])

    if id==13:
        diagnosis_and_treatment = qa_chain_v(sys+chain+end_sys_prompts)
        diagnosis_and_treatment = str(diagnosis_and_treatment['result'])
        print(mode,diagnosis_and_treatment)
        report = qa_chain_a(report_prompt_template+sys+chain+"\n\ntreatment & diagnosis with ICD code below\n"+diagnosis_and_treatment)
        print(str(report['result']))
        print("h&P")
        return str(report['result'])
            
            
    return QA()