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import os
import streamlit as st
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.groq import Groq
from crewai import Agent, Task, Crew
from crewai_tools import LlamaIndexTool
from langchain_openai import ChatOpenAI
from langchain_groq import ChatGroq
import tempfile
st.set_page_config(page_title="Financial Analyst App", layout="wide")
# Environment API Keys
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
# Streamlit Input for API Keys
st.title("Financial Analysis and Content Generation App")
if not GROQ_API_KEY or not TAVILY_API_KEY:
st.warning("Please enter valid API keys to proceed.")
st.stop()
# File Upload
uploaded_file = st.file_uploader("Upload a PDF for Analysis", type="pdf")
if uploaded_file:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.read())
pdf_path = tmp_file.name
st.success("PDF uploaded successfully!")
# Load and Embed the Document
st.subheader("Processing PDF...")
reader = SimpleDirectoryReader(input_files=[pdf_path])
docs = reader.load_data()
st.write("Loaded document content: ", docs[0].text[:500])
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
index = VectorStoreIndex.from_documents(docs, embed_model=embed_model)
query_engine = index.as_query_engine(similarity_top_k=5)
st.subheader("Setting Up Query Tool")
#llm = ChatGroq(groq_api_key=os.getenv("GROQ_API_KEY"), model="groq/llama-3.2-90b-text-preview")
query_tool = LlamaIndexTool.from_query_engine(
query_engine,
name="Financial Query Tool",
description="Use this tool to lookup insights from the uploaded document.",
)
st.success("Query Engine is ready!")
# Agent Definitions
chat_llm = ChatOpenAI(
openai_api_base="https://api.groq.com/openai/v1",
openai_api_key=GROQ_API_KEY,
model="groq/llama-3.2-90b-text-preview",
temperature=0,
max_tokens=1000,
)
researcher = Agent(
role="Senior Financial Analyst",
goal="Uncover insights about the document",
backstory="You are an experienced analyst focused on extracting key financial insights.",
verbose=True,
allow_delegation=False,
tools=[query_tool],
llm=chat_llm,
)
writer = Agent(
role="Tech Content Strategist",
goal="Write an engaging blog post based on financial insights",
backstory="You transform complex financial information into accessible and engaging narratives.",
llm=chat_llm,
verbose=True,
allow_delegation=False,
)
# Tasks
task1 = Task(
description="Conduct a comprehensive analysis of the uploaded document.",
expected_output="Full analysis report in bullet points",
agent=researcher,
)
task2 = Task(
description="""Using the analysis insights, create an engaging blog post that highlights key findings
in a simple and accessible manner.""",
expected_output="A well-structured blog post with at least 4 paragraphs.",
agent=writer,
)
# Crew Execution
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=True,
)
if st.button("Kickoff Analysis"):
st.subheader("Running Analysis and Content Generation...")
result = crew.kickoff()
st.subheader("Generated Output:")
st.write(result)
else:
st.info("Please upload a PDF file to proceed.")
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