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##importing relevant libraries and modules
import os
import nltk
import requests
import gradio as gr
from pathlib import Path
from dotenv import load_dotenv
# Importing my personal rag packages and modules
from rag_builder.Ingesting_phase import DocumentLoader
from rag_builder.Retrival_phase import dv, reset_database
from rag_builder.LLM_Inference import get_response
nltk.download("punkt")
#this is to load the env
load_dotenv()
# buidling the gradio logic
def run_app(file_obj, url_input, user_query):
# Clearing out any previous input
reset_database()
# handling the ingestion
if url_input:
html = requests.get(url_input).text
temp_path = Path("./temp_url.html")
temp_path.write_text(html, encoding="utf-8")
loader = DocumentLoader(str(temp_path))
orig_chunks, proc_chunks = loader.load_html()
dv.original_docs.extend(orig_chunks)
dv.add_documents(proc_chunks)
temp_path.unlink()
elif file_obj:
ext = Path(file_obj.name).suffix.lower().lstrip('.')
loader = DocumentLoader(file_obj.name)
if ext == 'pdf':
orig_chunks, proc_chunks = loader.load_pdf()
elif ext == 'txt':
orig_chunks, proc_chunks = loader.load_text()
else:
return "Unsupported file type.\nPlease upload PDF or TXT.", ""
dv.original_docs.extend(orig_chunks)
dv.add_documents(proc_chunks)
else:
return "Please upload a file or enter a URL.", ""
# Base model output to handle cases with no context
base_output = get_response(user_query, "")
##gathering the best matches as context
matches = dv.find_best_matches(user_query)
flat_context = []
for m in matches:
if isinstance(m, list):
flat_context.extend(m)
else:
flat_context.append(m)
context = "".join(flat_context)
rag_output = get_response(user_query, context)
return base_output, rag_output
# buidling the gradio interface
def main():
with gr.Blocks() as demo:
gr.Markdown("## RAG vs. Base Model Comparison: Kindly Provide A Document or A Link And Ask Questions")
with gr.Row():
file_input = gr.File(label="Upload PDF/TXT", file_types=[".pdf", ".txt"])
url_input = gr.Textbox(label="Or enter HTML URL", placeholder="https://...")
query_input = gr.Textbox(label="Ask a question:")
run_btn = gr.Button("Run")
out_base = gr.Textbox(label="Base Model Output", lines=5)
out_rag = gr.Textbox(label="RAG-Enhanced Output", lines=5)
run_btn.click(fn=run_app,
inputs=[file_input, url_input, query_input],
outputs=[out_base, out_rag])
demo.launch(share= True)
if __name__ == "__main__":
main()
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