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import os
import gradio as gr
from langchain_groq import ChatGroq
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain.chains import create_retrieval_chain
from langchain_community.vectorstores import FAISS
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from dotenv import load_dotenv
load_dotenv()
# Load the GROQ API KEY
os.environ['GROQ_API_KEY'] = GROQ_API_KEY
llm = ChatGroq(temperature=0, model_name='llama-3.1-8b-instant', groq_api_key=GROQ_API_KEY)
prompt = ChatPromptTemplate.from_template(
"""
Answer the questions based on the provided context only.
Please provide the most accurate response based on the question
<context>
{context}
</context>
Question: {input}
"""
)
embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectors = None
def process_pdf(file):
global vectors
if file is not None:
loader = PyPDFLoader(file.name)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
final_documents = text_splitter.split_documents(docs)
if vectors is None:
vectors = FAISS.from_documents(final_documents, embeddings)
else:
vectors.add_documents(final_documents)
return "PDF processed and added to the knowledge base."
return "No file uploaded."
def process_question(question):
if vectors is None:
return "Please upload a PDF first.", "", 0
document_chain = create_stuff_documents_chain(llm, prompt)
retriever = vectors.as_retriever()
retrieval_chain = create_retrieval_chain(retriever, document_chain)
response = retrieval_chain.invoke({'input': question})
context = "\n\n".join([doc.page_content for doc in response["context"]])
# Calculate a simple confidence score based on the relevance of retrieved documents
confidence_score = sum([doc.metadata.get('score', 0) for doc in response["context"]]) / len(response["context"])
return response['answer'], context, round(confidence_score, 2)
CSS = """
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
h3, p, h1 {
text-align: center;
color: white;
}
footer {
text-align: center;
padding: 10px;
width: 100%;
background-color: rgba(240, 240, 240, 0.8);
z-index: 1000;
position: relative;
margin-top: 10px;
color: black;
}
"""
FOOTER_TEXT = """
<footer>
<p>If you enjoyed the functionality of the app, please leave a like!<br>
Check out more on <a href="https://www.linkedin.com/in/your-linkedin/" target="_blank">LinkedIn</a> |
<a href="https://your-portfolio-url.com/" target="_blank">Portfolio</a></p>
</footer>
"""
TITLE = "<h1>📚 RAG Document Q&A 📚</h1>"
with gr.Blocks(css=CSS, theme="Nymbo/Nymbo_Theme") as demo:
gr.HTML(TITLE)
with gr.Tab("PDF Uploader"):
pdf_file = gr.File(label="Upload PDF")
upload_button = gr.Button("Process PDF")
upload_output = gr.Textbox(label="Upload Status")
with gr.Tab("Q&A System"):
question_input = gr.Textbox(lines=2, placeholder="Enter your question here...")
submit_button = gr.Button("Ask Question")
answer_output = gr.Textbox(label="Answer")
context_output = gr.Textbox(label="Relevant Context", lines=10)
confidence_output = gr.Number(label="Confidence Score")
upload_button.click(process_pdf, inputs=[pdf_file], outputs=[upload_output])
submit_button.click(process_question, inputs=[question_input], outputs=[answer_output, context_output, confidence_output])
gr.HTML(FOOTER_TEXT)
if __name__ == "__main__":
demo.launch() |