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import os
from langchain_huggingface import HuggingFaceEndpoint
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
from langchain_core.runnables import RunnableLambda
LLM_REPO_ID = os.environ.get("LLM_REPO_ID", "Jyo-K/Fine-Tuned-Qwen2.5_1B")
HF_API_KEY = os.environ.get("HF_API_KEY")
_llm_instance = None
def get_llm():
"""Lazily initializes and returns the LLM instance."""
global _llm_instance
if _llm_instance is None:
if not HF_API_KEY:
raise ValueError("HF_TOKEN environment variable not set. Cannot initialize LLM.")
_llm_instance = HuggingFaceEndpoint(
#model= "Jyo-K/Fine-Tuned-Qwen2.5_1B",
repo_id=LLM_REPO_ID,
huggingfacehub_api_token=HF_API_KEY,
temperature=0.1,
max_new_tokens=256,
top_k=50,
top_p=0.95
)
print("--- LLM Initialized ---")
return _llm_instance
classifier_prompt = ChatPromptTemplate.from_messages([
("system", (
"You are a helpful classification assistant. "
"Your task is to classify the user's last response as 'yes', 'no', or 'unclear' "
"based on their message. "
"User's previous message: '{last_human_message}'"
"\nRespond ONLY with a single JSON object in the format: "
"{{\"classification\": \"yes\"}} or {{\"classification\": \"no\"}} or {{\"classification\": \"unclear\"}}"
))
])
symptom_classifier_chain = classifier_prompt | get_llm() | JsonOutputParser()
question_prompt = ChatPromptTemplate.from_messages([
("system", (
"You are a friendly medical assistant bot. Ask the user if they are experiencing the "
"following symptom. Be clear and concise. Do not add any extra greeting or sign-off. "
"Symptom: '{symptom}'"
"\nExample: Are you experiencing any itchiness or a rash?"
))
])
question_generation_chain = question_prompt | get_llm() | StrOutputParser()
summary_prompt = ChatPromptTemplate.from_messages([
("system", (
"You are a helpful medical assistant providing a summary. "
"Based on the initial image analysis and confirmed symptoms, generate a summary. "
"DO NOT provide a definitive diagnosis. "
"Structure your response clearly: "
"1. Start by stating the potential condition identified from the image."
"2. List the symptoms the user confirmed."
"3. Provide the general treatment information found for this condition."
"4. **ALWAYS** include the provided disclaimer at the very end."
"\n---"
"Initial Image Prediction: {disease}"
"Confirmed Symptoms: {symptoms}"
"Potential Treatment Information: {treatment}"
"Disclaimer: {disclaimer}"
"\n---"
"Generate your summary now."
))
])
summary_generation_chain = summary_prompt | get_llm() | StrOutputParser()
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