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import gradio as gr
from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate
from llama_index.llms.huggingface import HuggingFaceInferenceAPI
from dotenv import load_dotenv
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
import os
import tempfile
# Load environment variables
load_dotenv()
# Configure the Llama index settings
Settings.llm = HuggingFaceInferenceAPI(
model_name="google/gemma-1.1-7b-it",
tokenizer_name="google/gemma-1.1-7b-it",
context_window=3000,
token=os.getenv("HF_TOKEN"),
max_new_tokens=512,
generate_kwargs={"temperature": 0.1},
)
Settings.embed_model = HuggingFaceEmbedding(
model_name="BAAI/bge-small-en-v1.5"
)
# Define the directory for persistent storage and data
PERSIST_DIR = "./db"
DATA_DIR = "data"
# Ensure data directory exists
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(PERSIST_DIR, exist_ok=True)
def data_ingestion():
documents = SimpleDirectoryReader(DATA_DIR).load_data()
storage_context = StorageContext.from_defaults()
index = VectorStoreIndex.from_documents(documents)
index.storage_context.persist(persist_dir=PERSIST_DIR)
def handle_query(query):
storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
index = load_index_from_storage(storage_context)
chat_text_qa_msgs = [
(
"user",
"""You are a Q&A assistant named EazyPeazy, For all other inquiries, your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, kindly advise the user to ask questions within the context of the document.
Context:
{context_str}
Question:
{query_str}
"""
)
]
text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
query_engine = index.as_query_engine(text_qa_template=text_qa_template)
answer = query_engine.query(query)
if hasattr(answer, 'response'):
return answer.response
elif isinstance(answer, dict) and 'response' in answer:
return answer['response']
else:
return "Sorry, I couldn't find an answer."
def process_file(file):
if file is None:
return "Please upload a PDF file."
temp_dir = tempfile.mkdtemp()
temp_path = os.path.join(temp_dir, "uploaded.pdf")
with open(temp_path, "wb") as f:
f.write(file.read())
# Copy the file to the DATA_DIR
os.makedirs(DATA_DIR, exist_ok=True)
dest_path = os.path.join(DATA_DIR, "saved_pdf.pdf")
os.replace(temp_path, dest_path)
# Process the uploaded PDF
data_ingestion()
return "PDF processed successfully. You can now ask questions about its content."
def chatbot(message, history):
response = handle_query(message)
history.append((message, response))
return history, ""
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# (PDF) Information and Inference🗞️")
gr.Markdown("Retrieval-Augmented Generation")
with gr.Row():
with gr.Column(scale=1):
file_output = gr.Textbox(label="Upload Status")
upload_button = gr.UploadButton("Upload PDF", file_types=[".pdf"])
upload_button.upload(process_file, upload_button, file_output)
with gr.Column(scale=2):
chatbot = gr.Chatbot(
[],
elem_id="chatbot",
bubble_full_width=False,
)
msg = gr.Textbox(label="Ask me anything about the content of the PDF:")
clear = gr.Button("Clear")
msg.submit(chatbot, [msg, chatbot], [chatbot, msg])
clear.click(lambda: None, None, chatbot, queue=False)
if __name__ == "__main__":
demo.launch() |