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# src/app.py
import gradio as gr
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
from langchain_community.llms import HuggingFacePipeline
from retriever import get_retriever
from transformers import pipeline
import os
# Load local HuggingFace pipeline
pipe = pipeline(
"text-generation",
model="tiiuae/falcon-7b-instruct",
trust_remote_code=True,
device_map="auto",
max_new_tokens=512,
temperature=0.2
)
llm = HuggingFacePipeline(pipeline=pipe)
retriever = get_retriever()
# Prompt template
template = """
You are a legal assistant. Use the provided context to answer the question.
If language mode is Nigerian Pidgin, respond in Nigerian Pidgin.
Question: {question}
Context:
{context}
Answer:
"""
prompt = PromptTemplate(
input_variables=["question", "context"],
template=template
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
chain_type="stuff",
return_source_documents=True, # Needed to list references
chain_type_kwargs={"prompt": prompt}
)
def answer_question(user_input, lang_choice):
if lang_choice == "pidgin":
user_input = f"Respond in Nigerian Pidgin: {user_input}"
result = qa_chain(user_input)
answer_text = result["result"]
# Collect unique source file names
sources = list({doc.metadata.get("source", "Unknown") for doc in result["source_documents"]})
sources_list = "\n".join(f"- {src}" for src in sources)
return f"{answer_text}\n\nReferences:\n{sources_list}"
def launch_interface():
iface = gr.Interface(
fn=answer_question,
inputs=[
gr.Textbox(label="Your question"),
gr.Radio(["english", "pidgin"], label="Language")
],
outputs=gr.Textbox(label="Answer"),
title="KnowYourRight Bot",
description="Ask legal rights questions in English or Nigerian Pidgin with references"
)
iface.launch()
if __name__ == "__main__":
launch_interface()
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