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import gradio as gr
import fitz # PyMuPDF for PDF text extraction
import faiss # FAISS for vector search
import numpy as np
from sentence_transformers import SentenceTransformer
from huggingface_hub import InferenceClient
from typing import List, Tuple
# Default settings
class ChatConfig:
MODEL = "google/gemma-3-27b-it"
DEFAULT_SYSTEM_MSG = "You are an AI assistant answering only based on the uploaded PDF."
DEFAULT_MAX_TOKENS = 512
DEFAULT_TEMP = 0.3
DEFAULT_TOP_P = 0.95
client = InferenceClient(ChatConfig.MODEL)
embed_model = SentenceTransformer("all-MiniLM-L6-v2") # Lightweight embedding model
vector_dim = 384 # Embedding size
index = faiss.IndexFlatL2(vector_dim) # FAISS index
documents = [] # Store extracted text
def extract_text_from_pdf(pdf_path):
"""Extracts text from PDF"""
doc = fitz.open(pdf_path)
text_chunks = [page.get_text("text") for page in doc]
return text_chunks
def create_vector_db(text_chunks):
"""Embeds text chunks and adds them to FAISS index"""
global documents, index
documents = text_chunks
embeddings = embed_model.encode(text_chunks)
index.add(np.array(embeddings, dtype=np.float32))
def search_relevant_text(query):
"""Finds the most relevant text chunk for the given query"""
query_embedding = embed_model.encode([query])
_, closest_idx = index.search(np.array(query_embedding, dtype=np.float32), k=3)
return "\n".join([documents[i] for i in closest_idx[0]])
def generate_response(
message: str,
history: List[Tuple[str, str]],
system_message: str = ChatConfig.DEFAULT_SYSTEM_MSG,
max_tokens: int = ChatConfig.DEFAULT_MAX_TOKENS,
temperature: float = ChatConfig.DEFAULT_TEMP,
top_p: float = ChatConfig.DEFAULT_TOP_P
) -> str:
if not documents:
return "Please upload a PDF first."
context = search_relevant_text(message) # Get relevant content from PDF
messages = [{"role": "system", "content": system_message}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": f"Context: {context}\nQuestion: {message}"})
response = ""
for chunk in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = chunk.choices[0].delta.content or ""
response += token
yield response
def handle_upload(pdf_file):
"""Handles PDF upload and creates vector DB"""
text_chunks = extract_text_from_pdf(pdf_file.name)
create_vector_db(text_chunks)
return "PDF uploaded and indexed successfully!"
def create_interface() -> gr.Blocks:
"""Creates the Gradio interface"""
with gr.Blocks() as interface:
gr.Markdown("# PDF-Based Chatbot using Google Gemma")
with gr.Row():
chatbot = gr.Chatbot(label="Chat with Your PDF")
pdf_upload = gr.File(label="Upload PDF", type="file")
with gr.Row():
user_input = gr.Textbox(label="Ask a question", placeholder="Type here...")
send_button = gr.Button("Send")
output = gr.Textbox(label="Response", lines=5)
# Upload PDF handler
pdf_upload.change(handle_upload, inputs=[pdf_upload], outputs=[])
# Chat function
send_button.click(
generate_response,
inputs=[user_input, chatbot],
outputs=[output]
)
return interface
if __name__ == "__main__":
app = create_interface()
app.launch()
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