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SciQu.py
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SciQu.py
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import streamlit as st
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.embeddings import OllamaEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.prompts import ChatPromptTemplate, PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_community.chat_models import ChatOllama
from langchain.retrievers.multi_query import MultiQueryRetriever
from langchain_core.runnables import RunnablePassthrough
import tempfile
import os
import random
import requests
import os
import streamlit as st
# Other imports...
# Get the directory of the current script
script_dir = os.path.dirname(os.path.abspath(__file__))
image_path = os.path.join(script_dir, 'bg.png.webp')
# Streamlit UI setup
st.set_page_config(page_title="BabuBot", page_icon="🤖", layout="wide")
st.markdown(
"""
<style>
.main {
background: linear-gradient(to right, #fbc2eb, #a6c1ee);
color: black;
}
.stButton>button {
background-color: #4CAF50;
color: white;
border: none;
padding: 15px 32px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: 16px;
margin: 4px 2px;
cursor: pointer;
border-radius: 12px;
}
.stFileUploader {
background-color: #ff9800;
color: white;
border: none;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
border-radius: 12px;
}
</style>
""",
unsafe_allow_html=True,
)
st.title("Welcome to BabuBot")
st.image(image_path, use_column_width=True) # Use the relative path
st.write("Upload a PDF file and ask questions to retrieve relevant document sections.")
# The rest of your script...
# Initialize components
local_model = "mistral"
llm = ChatOllama(model=local_model)
QUERY_PROMPT = PromptTemplate(
input_variables=["question"],
template="""You are an AI language model assistant. Your task is to generate five
different versions of the given user question to retrieve relevant documents from
a vector database. By generating multiple perspectives on the user question, your
goal is to help the user overcome some of the limitations of the distance-based
similarity search. Provide these alternative questions separated by newlines.
Original question: {question}""",
)
template = """Answer the question based ONLY on the following context:
{context}
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
# Streamlit UI setup
st.set_page_config(page_title="BabuBot", page_icon="🤖", layout="wide")
st.markdown(
"""
<style>
.main {
background: linear-gradient(to right, #fbc2eb, #a6c1ee);
color: black;
}
.stButton>button {
background-color: #4CAF50;
color: white;
border: none;
padding: 15px 32px;
text-align: center;
text-decoration: none;
display: inline-block;
font-size: 16px;
margin: 4px 2px;
cursor: pointer;
border-radius: 12px;
}
.stFileUploader {
background-color: #ff9800;
color: white;
border: none;
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
border-radius: 12px;
}
</style>
""",
unsafe_allow_html=True,
)
st.title("Welcome to BabuBot")
st.image("bg.png.webp", use_column_width=True) # Replace with your image URL
st.write("Upload a PDF file and ask questions to retrieve relevant document sections.")
# Session state to store history
if 'history' not in st.session_state:
st.session_state['history'] = []
# Colors for different questions and answers
colors = ["#ff4b4b", "#4bffa5", "#4bb3ff", "#f4ff4b", "#ffb84b"]
# File upload
uploaded_file = st.file_uploader("Upload a PDF", type="pdf")
if uploaded_file:
st.info("Uploading and processing PDF file...")
progress_bar = st.progress(0)
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
tmp_file.write(uploaded_file.read())
temp_file_path = tmp_file.name
progress_bar.progress(10)
try:
# Process the PDF
st.info("Processing the PDF...")
loader = UnstructuredPDFLoader(file_path=temp_file_path)
data = loader.load()
progress_bar.progress(30)
st.info("Splitting the document into chunks...")
text_splitter = RecursiveCharacterTextSplitter(chunk_size=700, chunk_overlap=100)
chunks = text_splitter.split_documents(data)
progress_bar.progress(50)
# Clean up the temporary file
os.remove(temp_file_path)
progress_bar.progress(60)
st.info("Creating vector database from document chunks...")
vector_db = Chroma.from_documents(
documents=chunks,
embedding=OllamaEmbeddings(model="nomic-embed-text", show_progress=True),
collection_name="local-rag"
)
retriever = MultiQueryRetriever.from_llm(
vector_db.as_retriever(),
llm,
prompt=QUERY_PROMPT
)
progress_bar.progress(80)
st.success("PDF file processed successfully!")
progress_bar.progress(100)
except Exception as e:
st.error(f"An error occurred: {e}")
progress_bar.progress(0)
# Query input
query = st.text_input("Ask a question about the document")
if query:
st.info("Retrieving information...")
# Determine if the query is related to the Materials Project
if "materials project" in query.lower():
st.write("Retrieving additional information from the Materials Project...")
api_key = "UOC1KWzQHn3ZtIYsU30ykzNBqXRWWn6X" # Make sure to replace with your actual API key
response = requests.get(
f"https://materialsproject.org/rest/v2/materials/{query}/vasp?API_KEY={api_key}"
)
if response.status_code == 200:
materials_data = response.json()
st.write("Materials Project Data:")
st.json(materials_data)
else:
st.write("Failed to retrieve data from the Materials Project API.")
else:
# Process the query with the local RAG model
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
result = chain.invoke({"question": query})
st.session_state['history'].append((query, result))
st.write("Answer:")
st.write(result)
# Display history
if st.session_state['history']:
st.write("## Question and Answer History")
for q, a in st.session_state['history']:
color = random.choice(colors)
st.markdown(f"<div style='background-color: {color}; padding: 10px; border-radius: 10px;'>"
f"<strong>Question:</strong> {q}<br>"
f"<strong>Answer:</strong> {a}</div>", unsafe_allow_html=True)
st.write("---")