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import gradio as gr
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.vectorstores import FAISS
from langchain.memory import ConversationBufferMemory
from groq import Groq
import requests
from bs4 import BeautifulSoup
import time # To simulate progress updates
client = Groq(api_key="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")
embedding_model = SentenceTransformerEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
def process_pdf_with_langchain(pdf_path, progress_callback):
# progress_callback("Initializing PDF processing... 0%")
time.sleep(0.5)
loader = PyPDFLoader(pdf_path)
# progress_callback("Loading PDF... 20%")
documents = loader.load()
time.sleep(0.5)
# progress_callback("Splitting documents... 50%")
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
split_documents = text_splitter.split_documents(documents)
time.sleep(0.5)
# progress_callback("Creating vector store... 80%")
vectorstore = FAISS.from_documents(split_documents, embedding_model)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
progress_callback("Processing complete! 100%")
return retriever
def generate_response(query, retriever=None, use_web_search=False):
knowledge = ""
if retriever:
relevant_docs = retriever.get_relevant_documents(query)
knowledge += "\n".join([doc.page_content for doc in relevant_docs])
if use_web_search:
web_results = scrape_google_search(query)
knowledge += f"\n\nWeb Search Results:\n{web_results}"
chat_history = memory.load_memory_variables({}).get("chat_history", "")
context = (
f"This is a conversation with ParvizGPT, an AI model designed by Amir Mahdi Parviz from Kermanshah University of Technology (KUT), "
f"to help with tasks like answering questions in Persian, providing recommendations, and decision-making."
)
if knowledge:
context += f"\n\nRelevant Knowledge:\n{knowledge}"
if chat_history:
context += f"\n\nChat History:\n{chat_history}"
context += f"\n\nYou: {query}\nParvizGPT:"
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": context}],
model="llama-3.3-70b-versatile",
)
response = chat_completion.choices[0].message.content.strip()
memory.save_context({"input": query}, {"output": response})
return response
def upload_and_process(file, progress_display):
try:
global retriever
progress_updates = []
retriever = process_pdf_with_langchain(file.name, lambda msg: progress_updates.append(msg))
return "\n".join(progress_updates), "File uploaded and processed successfully."
except Exception as e:
return "", f"Error processing file: {e}"
def gradio_interface(user_message, chat_box, enable_web_search=False):
global retriever
response = generate_response(user_message, retriever=retriever, use_web_search=enable_web_search)
chat_box.append(("You", user_message))
chat_box.append(("ParvizGPT", response))
return chat_box
def clear_memory():
memory.clear()
return []
retriever = None
with gr.Blocks() as interface:
gr.Markdown("## ParvizGPT")
with gr.Row():
chat_box = gr.Chatbot(label="Chat History", value=[])
with gr.Row():
user_message = gr.Textbox(
label="Your Message",
placeholder="Type your message here and press Enter...",
lines=1,
interactive=True,
)
with gr.Row():
clear_memory_btn = gr.Button("Clear Memory", interactive=True)
enable_web_search = gr.Checkbox(label="🌐Enable Web Search", value=False, interactive=True)
with gr.Row():
pdf_upload = gr.UploadButton(label="📄 Upload Your PDF", file_types=[".pdf"])
progress_display = gr.Textbox(label="Progress", placeholder="Progress updates will appear here", interactive=True)
with gr.Row():
submit_btn = gr.Button("Submit")
pdf_upload.upload(upload_and_process, inputs=[pdf_upload, progress_display], outputs=[progress_display])
submit_btn.click(gradio_interface, inputs=[user_message, chat_box, enable_web_search], outputs=chat_box)
user_message.submit(gradio_interface, inputs=[user_message, chat_box, enable_web_search], outputs=chat_box)
clear_memory_btn.click(clear_memory, inputs=[], outputs=chat_box)
interface.launch() |