# Ask your pdf with llama3 locally

# Import necessary libraries

import os

import tempfile

import streamlit as st

from embedchain import App

# Define the embedchain_bot function

def embedchain_bot(db_path):

return App.from_config(

config={

"llm": {"provider": "ollama", "config": {"model": "llama3:instruct", "max_tokens": 250, "temperature": 0.5, "stream": True, "base_url": 'http://localhost:11434'}},

"vectordb": {"provider": "chroma", "config": {"dir": db_path}},

"embedder": {"provider": "ollama", "config": {"model": "llama3:instruct", "base_url": 'http://localhost:11434'}},

}

)

st.title("Chat with PDF")

st.caption("This app allows you to chat with a PDF using Llama3 running locally wiht Ollama!")

# Create a temporary directory to store the PDF file

db_path = tempfile.mkdtemp()

# Create an instance of the embedchain App

app = embedchain_bot(db_path)

# Upload a PDF file

pdf_file = st.file_uploader("Upload a PDF file", type="pdf")

# If a PDF file is uploaded, add it to the knowledge base

if pdf_file:

with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as f:

f.write(pdf_file.getvalue())

app.add(f.name, data_type="pdf_file")

os.remove(f.name)

st.success(f"Added {pdf_file.name} to knowledge base!")

# Ask a question about the PDF

prompt = st.text_input("Ask a question about the PDF")

# Display the answer

if prompt:

answer = app.chat(prompt)

st.write(answer)