git clone <https://github.com/vishwasg217/finsight.git>cd finsight

# For macOS and Linux:python3 -m venv venv
# For Windows:python -m venv venv

# For macOS and Linux:source venv/bin/activate# For Windows:.\\venv\\Scripts\\activate

pip install -r requirements.txt

# create directorymkdir .streamlit# create toml filetouch .streamlit/secrets.toml

# Add the following API keysav_api_key = "ALPHA_VANTAGE API KEY"openai_api_key = "OPEN AI API KEY"

from pypdf import PdfReaderfrom llama_index.schema import Document
def process_pdf(pdf):file = PdfReader(pdf)text = ""for page in file.pages:text += str(page.extract_text())
doc = Document(text=text)return [doc]

from llama_index.llms import OpenAIfrom llama_index import VectorStoreIndex, ServiceContext, StorageContextfrom llama_index.vector_stores import FaissVectorStore
def get_vector_index(documents):llm = OpenAI(OPENAI_API_KEY)faiss_index = faiss.IndexFlatL2(d)vector_store = FaissVectorStore(faiss_index=faiss_index)
storage_context = StorageContext.from_defaults(vector_store=vector_store)service_context = ServiceContext.from_defaults(llm=llm)
index = VectorStoreIndex.from_documents(documents,service_context=service_context,storage_context=storage_context)
return index

# Calling the functions through streamlit frontend
import streamlit as st
if "index" not in st.session_state:st.session_state.index = None
if "process_doc" not in st.session_state:st.session_state.process_doc = False
if st.sidebar.button("Process Document"):with st.spinner("Processing Document..."):documents = process_pdf(pdfs)st.session_state.index = get_vector_index(documents)st.session_state.process_doc = True
st.toast("Document Processsed!")

index = get_vector_index(documents)engine = index.as_query_engine()query = "How has Microsoft performed in this fiscal year?"response = engine(query)

def get_query_engine(engine):query_engine_tools = [QueryEngineTool(query_engine=engine,metadata=ToolMetadata(name="Annual Report",description=f"Provides information about the company from its annual report.",),),]s_engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=query_engine_tools)return s_engine
index = get_vector_index(documents)engine = index.as_query_engine()s_engine = get_query_engine(engine)

# example for multiple query engine toolsquery_engine_tools = [QueryEngineTool(query_engine=sept_engine,metadata=ToolMetadata(name="sept_22",description="Provides information about Uber quarterly financials ending September 2022",),),QueryEngineTool(query_engine=june_engine,metadata=ToolMetadata(name="june_22",description="Provides information about Uber quarterly financials ending June 2022",),)]

from pydantic import BaseModel, Field
class FiscalYearHighlights(BaseModel):performance_highlights: str = Field(..., description="Key performance metrics and financial stats over the fiscal year.")major_events: str = Field(..., description="Highlight of significant events, acquisitions, or strategic shifts that occurred during the year.")challenges_encountered: str = Field(..., description="Challenges the company faced during the year and, if and how they managed or overcame them.")
class StrategyOutlookFutureDirection(BaseModel):strategic_initiatives: str = Field(..., description="The company's primary objectives and growth strategies for the upcoming years.")market_outlook: str = Field(..., description="Insights into the broader market, competitive landscape, and industry trends the company anticipates.")
class RiskManagement(BaseModel):risk_factors: str = Field(..., description="Primary risks the company acknowledges.")risk_mitigation: str = Field(..., description="Strategies for managing these risks.")
class InnovationRnD(BaseModel):r_and_d_activities: str = Field(..., description="Overview of the company's focus on research and development, major achievements, or breakthroughs.")innovation_focus: str = Field(..., description="Mention of new technologies, patents, or areas of research the company is diving into.")

from langchain.prompts import PromptTemplatefrom langchain.output_parsers import PydanticOutputParser
prompt_template = """You are given the task of generating insights for {section} from the annual report of the company.
Given below is the output format, which has the subsections.Must use bullet points.Always use $ symbol for money values, and round it off to millions or billions accordingly
Incase you don't have enough info you can just write: No information available---{output_format}---"""
def report_insights(engine, section, pydantic_model):parser = PydanticOutputParser(pydantic_object=pydantic_model)
prompt_template = PromptTemplate(template=prompt_template,input_variables=["section"],partial_variables={"output_format": parser.get_format_instructions()})
formatted_input = prompt_template.format(section=section)response = engine.query(formatted_input)parsed_response = parser.parse(response.response)
return parsed_response

# calling the function in streamlit frontend
if st.session_state.process_doc:
if st.button("Analyze Report"):
engine = get_query_engine(st.session_state.index.as_query_engine(similarity_top_k=3))
with st.status("**Analyzing Report...**"):
st.write("Fiscal Year Highlights...")st.session_state.fiscal_year_highlights = report_insights(engine, "Fiscal Year Highlights", FiscalYearHighlights)
st.write("Strategy Outlook and Future Direction...")st.session_state.strategy_outlook_future_direction = report_insights(engine, "Strategy Outlook and Future Direction", StrategyOutlookFutureDirection)
st.write("Risk Management...")st.session_state.risk_management = report_insights(engine, "Risk Management", RiskManagement)
st.write("Innovation and R&D...")st.session_state.innovation_and_rd = report_insights(engine, "Innovation and R&D", InnovationRnD)
# displaying the generated insights
if st.session_state.fiscal_year_highlights:
with tab1:st.write("## Fiscal Year Highlights")st.write("### Performance Highlights")st.write(st.session_state.fiscal_year_highlights.performance_highlights)st.write("### Major Events")st.write(st.session_state.fiscal_year_highlights.major_events)st.write("### Challenges Encountered")st.write(st.session_state.fiscal_year_highlights.challenges_encountered)st.write("### Milestone Achievements")st.write(str(st.session_state.fiscal_year_highlights.milestone_achievements))
if st.session_state.strategy_outlook_future_direction:with tab2:st.write("## Strategy Outlook and Future Direction")st.write("### Strategic Initiatives")st.write(st.session_state.strategy_outlook_future_direction.strategic_initiatives)st.write("### Market Outlook")st.write(st.session_state.strategy_outlook_future_direction.market_outlook)st.write("### Product Roadmap")st.write(st.session_state.strategy_outlook_future_direction.product_roadmap)
if st.session_state.risk_management:with tab3:st.write("## Risk Management")st.write("### Risk Factors")st.write(st.session_state.risk_management.risk_factors)st.write("### Risk Mitigation")st.write(st.session_state.risk_management.risk_mitigation)
if st.session_state.innovation_and_rd:with tab4:st.write("## Innovation and R&D")st.write("### R&D Activities")st.write(st.session_state.innovation_and_rd.r_and_d_activities)st.write("### Innovation Focus")st.write(st.session_state.innovation_and_rd.innovation_focus)