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import gradio as gr
from langchain import HuggingFaceHub
from langchain_community.document_loaders import WebBaseLoader, PyPDFLoader
from langchain_community.vectorstores import Chroma
from langchain_community import embeddings
from langchain_community.chat_models import ChatOllama
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain.output_parsers import PydanticOutputParser
from langchain.text_splitter import CharacterTextSplitter
import os
def process_input(urls, question):
os.environ['HUGGINGFACEHUB_API_TOKEN'] = 'HUGGINGFACEHUB_API_TOKEN'
model_local = HuggingFaceHub(repo_id="google/gemma-7b", model_kwargs={"temperature":0,"max_length":64})
# Convert string of URLs to list
urls_list = urls.split("\n")
docs = [WebBaseLoader(url).load() for url in urls_list]
docs_list = [item for sublist in docs for item in sublist]
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(chunk_size=7500, chunk_overlap=100)
doc_splits = text_splitter.split_documents(docs_list)
vectorstore = Chroma.from_documents(
documents=doc_splits,
collection_name="rag-chroma",
embedding=embeddings.ollama.OllamaEmbeddings(model='nomic-embed-text'),
)
retriever = vectorstore.as_retriever()
after_rag_template = """Answer the question based only on the following context:
{context}
Question: {question}
"""
after_rag_prompt = ChatPromptTemplate.from_template(after_rag_template)
after_rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| after_rag_prompt
| model_local
| StrOutputParser()
)
return after_rag_chain.invoke(question)
# Define Gradio interface
iface = gr.Interface(fn=process_input,
inputs=[gr.Textbox(label="Enter URLs separated by new lines"), gr.Textbox(label="Question")],
# server_name
outputs="text",
title="Document Query with Ollama",
description="Enter URLs and a question to query the documents.")
iface.launch()
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