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import os
import streamlit as st
from crewai import Agent, Task, Crew
from crewai_tools import LlamaIndexTool
from langchain_groq import ChatGroq
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
import tempfile
import requests
# --- Streamlit UI Header ---
st.title("Document Q&A Assistant with CrewAI")
st.write("Upload a document, provide a link, or ask questions dynamically!")
# --- Key Configuration from Secrets ---
try:
GROQ_API_KEY = st.secrets["GROQ_API_KEY"]
TAVILY_API_KEY = st.secrets["TAVILY_API_KEY"]
except KeyError as e:
st.error(f"Missing API key in secrets: {e}. Please add it to your environment.")
st.stop()
# Check if all API keys are available
if not GROQ_API_KEY or not TAVILY_API_KEY:
st.error("One or more required API keys are missing. Please check your configuration.")
st.stop()
# Function to download PDF from URL
def download_pdf_from_url(url, save_path):
response = requests.get(url)
if response.status_code == 200:
with open(save_path, 'wb') as f:
f.write(response.content)
return save_path
else:
st.error("Failed to download PDF from the provided URL.")
return None
# --- User Inputs for File or Link ---
document_source = st.radio("Choose input method:", ("Upload a PDF", "Provide PDF URL"))
pdf_path = None
if document_source == "Upload a PDF":
uploaded_file = st.file_uploader("Upload a PDF file", type=['pdf'])
if uploaded_file:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(uploaded_file.getvalue())
pdf_path = temp_file.name
st.success("File uploaded successfully!")
else:
pdf_url = st.text_input("Enter PDF URL")
if st.button("Download PDF") and pdf_url:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
saved_path = download_pdf_from_url(pdf_url, temp_file.name)
if saved_path:
pdf_path = saved_path
# --- LLM Configuration ---
llm = ChatGroq(groq_api_key=GROQ_API_KEY, model="groq/llama-3.3-70b-versatile")
# Function to create Query Engine
def create_query_engine(pdf_path, llm):
reader = SimpleDirectoryReader(input_files=[pdf_path])
docs = reader.load_data()
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
index = VectorStoreIndex.from_documents(docs, embed_model=embed_model)
return index.as_query_engine(similarity_top_k=5)
# --- Streamlit Question Workflow ---
if pdf_path:
st.success("PDF loaded successfully!")
query_engine = create_query_engine(pdf_path, llm)
query_tool = LlamaIndexTool.from_query_engine(
query_engine,
name="Document Query Tool",
description="Tool to analyze and retrieve information from the uploaded document."
)
# Define Agents and Tasks
researcher = Agent(
role="Document Analyst",
goal="Analyze documents and answer questions",
backstory="Expert at retrieving insights from documents.",
verbose=True,
allow_delegation=False,
tools=[query_tool],
llm=llm,
)
task = Task(
description="Answer user queries based on the uploaded document.",
expected_output="Clear and concise answers to user questions.",
agent=researcher,
)
crew = Crew(agents=[researcher], tasks=[task], verbose=True)
st.subheader("Ask a Question")
user_question = st.text_input("Enter your question")
if st.button("Get Answer"):
with st.spinner("Processing your request..."):
result = crew.kickoff(inputs={"question": user_question})
st.success("Here is the answer:")
st.write(result)
else:
st.warning("Please upload a PDF or provide a valid URL to continue.")
# --- Clean Up ---
if pdf_path and os.path.exists(pdf_path):
os.remove(pdf_path)
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