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import gradio as gr
from langchain.prompts import PromptTemplate
from langchain.embeddings import SentenceTransformerEmbeddings
# Set model_kwargs with trust_remote_code=True
embeddings = SentenceTransformerEmbeddings(
model_name="nomic-ai/nomic-embed-text-v1.5",
model_kwargs={"trust_remote_code": True}
)
print('Embeddings loaded successfully')
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader, PyPDFLoader
loader = PyPDFLoader("fibromyalgia-information-booklet-july2021.pdf")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
vector_store = FAISS.from_documents(docs, embeddings)
retriever = vector_store.as_retriever()
print('Retriever loaded successfully')
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("mohamedalcafory/PubMed_Llama3.1_Based_model")
model = AutoModelForCausalLM.from_pretrained("mohamedalcafory/PubMed_Llama3.1_Based_model")
print('Model loaded successfully')
from transformers import pipeline
from langchain_huggingface import HuggingFacePipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
llm = HuggingFacePipeline(pipeline=pipe)
prompt = PromptTemplate(
input_variables=["query"],
template="{query}"
)
# Define the retrieval chain
retrieve_docs = (lambda x: retriever.get_relevant_documents(x["query"]))
# Define the generator chain
generator_chain = (
prompt
| llm
| StrOutputParser()
)
def format_docs(docs):
# Check if docs is a list of Document objects or just strings
if docs and hasattr(docs[0], 'page_content'):
return "\n\n".join(doc.page_content for doc in docs)
else:
return "\n\n".join(str(doc) for doc in docs)
# Create the full RAG chain
rag_chain = (
RunnablePassthrough.assign(context=retrieve_docs)
| RunnablePassthrough.assign(
formatted_context=lambda x: format_docs(x["context"])
)
| prompt
| llm
| StrOutputParser()
)
def process_query(query):
try:
response = rag_chain.invoke({"query": query})
return response
except Exception as e:
return f"An error occurred: {str(e)}"
# Create Gradio interface
demo = gr.Interface(
fn=process_query,
inputs=gr.Textbox(label= "Your question", lines=2, placeholder="Enter your question here..."),
outputs=gr.Textbox(label="Response"),
title="Fibromyalgia Q&A Assistant",
description="Ask questions and get answers based on the retrieved context.",
examples=[
["How does Physiotherapy work with Fibromyalgia?"],
["What are the common treatments for chronic pain?"],
]
)
if __name__ == "__main__":
demo.launch() |