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import os
from langchain_community.document_loaders import PyPDFDirectoryLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import Chroma
from langchain_community.llms import LlamaCpp
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
import gradio as gr
# Environment variable for Hugging Face API token
os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# Paths for PDFs and model (upload these to the Hugging Face Space)
PDF_DIR = "./Data" # Replace with the path where you upload your PDFs
MODEL_PATH = "./BioMistral-7B.Q4_K_M.gguf" # Replace with the model's path in the Space
# Load and process PDF documents
loader = PyPDFDirectoryLoader(PDF_DIR)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=50)
chunks = text_splitter.split_documents(docs)
# Create embeddings and vector store
embeddings = SentenceTransformerEmbeddings(model_name="NeuML/pubmedbert-base-embeddings")
vectorstore = Chroma.from_documents(chunks, embeddings)
# Retriever for querying
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
# Initialize the LLM
llm = LlamaCpp(
model_path=MODEL_PATH,
temperature=0.2,
max_tokens=2048,
top_p=1
)
# Define the prompt template
template = """
<|context|>
You are a Medical Assistant that follows instructions and generates accurate responses based on the query and the context provided.
Please be truthful and give direct answers.
</s>
<|user|>
{query}
</s>
<|assistant|>
"""
prompt = ChatPromptTemplate.from_template(template)
# Define the RAG chain
rag_chain = (
{"context": retriever, "query": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
# Define a function for the Gradio UI
def chatbot_ui(user_query):
if not user_query.strip():
return "Please enter a valid query."
try:
result = rag_chain.invoke(user_query)
return result
except Exception as e:
return f"Error: {str(e)}"
# Create the Gradio interface
interface = gr.Interface(
fn=chatbot_ui,
inputs=gr.Textbox(label="Enter your medical query:", placeholder="Ask a medical question here..."),
outputs=gr.Textbox(label="Chatbot Response"),
title="Medical Assistant Chatbot",
description="A chatbot designed for heart patients, providing accurate and reliable medical information.",
examples=[
["What are the symptoms of diabetes?"],
["Explain the risk factors of heart disease."],
["How can I reduce cholesterol levels naturally?"],
]
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch()
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