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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
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):
"""Process the PDF file using LangChain for RAG."""
loader = PyPDFLoader(pdf_path)
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=50)
split_documents = text_splitter.split_documents(documents)
vectorstore = FAISS.from_documents(split_documents, embedding_model)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
return retriever
def scrape_google_search(query, num_results=3):
"""Search Google and return the top results."""
headers = {"User-Agent": "Mozilla/5.0"}
search_url = f"https://www.google.com/search?q={query}"
response = requests.get(search_url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")
results = []
for g in soup.find_all('div', class_='tF2Cxc')[:num_results]:
title = g.find('h3').text
link = g.find('a')['href']
results.append(f"{title}: {link}")
return "\n".join(results)
def generate_response(query, retriever=None, use_web_search=False):
"""Generate a response using LangChain with optional retriever and web search."""
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, "
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 gradio_interface(user_message, pdf_file=None, enable_web_search=False):
global retriever
if pdf_file is not None:
try:
retriever = process_pdf_with_langchain(pdf_file.name)
except Exception as e:
return f"Error processing PDF: {e}"
response = generate_response(user_message, retriever=retriever, use_web_search=enable_web_search)
return response
def clear_memory():
memory.clear()
return "Memory cleared!"
retriever = None
with gr.Blocks() as interface:
gr.Markdown("## ParvizGPT with Memory and Web Search Toggle")
with gr.Row():
user_message = gr.Textbox(label="Your Question", placeholder="Type your question here...", lines=1)
submit_btn = gr.Button("Submit")
with gr.Row():
pdf_file = gr.File(label="Upload PDF for Context (Optional)", type="filepath")
enable_web_search = gr.Checkbox(label="Enable Web Search", value=False)
with gr.Row():
clear_memory_btn = gr.Button("Clear Memory")
response_output = gr.Textbox(label="Response", placeholder="ParvizGPT's response will appear here.")
submit_btn.click(gradio_interface, inputs=[user_message, pdf_file, enable_web_search], outputs=response_output)
clear_memory_btn.click(clear_memory, inputs=[], outputs=response_output)
gr.HTML(
"""
<script>
document.addEventListener("keydown", function(event) {
if (event.key === "Enter" && !event.shiftKey) {
event.preventDefault();
document.querySelector('button[title="Submit"]').click();
}
});
</script>
"""
)
interface.launch()
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