Delete app.py

app.py

@@ -1,113 +0,0 @@
-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.")