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import pandas as pd
df = pd.read_csv('./medical_data.csv')
context_data = []
for i in range(len(df)):
context = ""
for j in range(3):
context += df.columns[j]
context += ": "
context += df.iloc[i][j]
context += " "
context_data.append(context)
import os
# Get the secret key from the environment
groq_key = os.environ.get('groq_api_keys')
## LLM used for RAG
from langchain_groq import ChatGroq
llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
## Embedding model!
from langchain_huggingface import HuggingFaceEmbeddings
embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
# create vector store!
from langchain_chroma import Chroma
vectorstore = Chroma(
collection_name="medical_dataset_store",
embedding_function=embed_model,
)
# add data to vector nstore
vectorstore.add_texts(context_data)
retriever = vectorstore.as_retriever()
from langchain_core.prompts import PromptTemplate
template = ("""You are a medical expert.
Use the provided context to answer the question.
If you don't know the answer, say so. Explain your answer in detail.
Do not discuss the context in your response; just provide the answer directly.
Context: {context}
Question: {question}
Answer:""")
rag_prompt = PromptTemplate.from_template(template)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| rag_prompt
| llm
| StrOutputParser()
)
import gradio as gr
def rag_memory_stream(message, history):
partial_text = ""
for new_text in rag_chain.stream(message):
partial_text += new_text
yield partial_text
examples = [
"I feel dizzy",
"what is the possible sickness for fatigue?"
]
description = "Real-time AI App with Groq API and LangChain to Answer medical questions"
title = "Medical Expert :) Try me!"
demo = gr.ChatInterface(fn=rag_memory_stream,
type="messages",
title=title,
description=description,
fill_height=True,
examples=examples,
theme="glass",
)
if __name__ == "__main__":
demo.launch()
|