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import gradio as gr
import os
from utils.document_parsing import DocParsing
from utils.retrieval import Retrieval
from utils.llm_generation import LLMGeneration
import json
embedding_model_name = "sentence-transformers/all-MiniLM-L6-v2"
# Setting up Retriever
retriever = Retrieval(model_name=embedding_model_name)
llm_model_name = "gpt-4o-mini"
# Settting up LLMGenerator
llm_generator = LLMGeneration(llm_model_name=llm_model_name)
def set_api_key(api_key):
os.environ['OPENAI_API_KEY'] = api_key
def process_inputs(api_key:str, pdf_file, questions: str):
# Setup Api KEY
set_api_key(api_key)
if pdf_file is None:
raise Exception("Blaf")
# Parsing the pdf
doc_handler = DocParsing(file_path=pdf_file.name,model_name=embedding_model_name)
docs = doc_handler.process_pdf()
# Create vector store
retriever.create_vector_store(chunks=docs)
output_dict = {}
questions_list = questions.strip().split('\n')
for question in questions_list:
# Retrieve top similar chunks
similar_chunks = retriever.search(query=question, k=10)
# Generate the answer
output_dict[question] = llm_generator.generate_answer(question, similar_chunks)
response = json.dumps(output_dict)
return response
with gr.Blocks() as demo:
gr.Markdown("# AskMYPDF Q&A App")
gr.Markdown("Enter your OPENAI API key, upload a PDF, and list your questions below.")
api_key_input = gr.Textbox(label="API Key", type="password")
pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
questions_input = gr.Textbox(label="List of Questions (one per line)", lines=5, placeholder="Question 1\nQuestion 2\n...")
submit_button = gr.Button("Submit")
output = gr.Textbox(label="Output")
submit_button.click(
fn=process_inputs,
inputs=[api_key_input, pdf_input, questions_input],
outputs=output
)
if __name__ == "__main__":
demo.launch() |