Create app.py

app.py

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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
+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 = Groq(model="llama3-70b-8192", api_key=GROQ_API_KEY)
+
+    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="llama3-70b-8192",
+        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=2,
+    )
+
+    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.")