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import pandas as pd
import json
from document_preprocessor import generate_document
from llm import LLM
from prompt import stock_analysis_prompt
import streamlit as st
from streamlit_searchbox import st_searchbox
st.set_page_config(
page_title="Stock_Picker",
page_icon="💰",
layout="wide",
initial_sidebar_state="expanded",
)
left_co, cent_co,last_co = st.columns(3)
with cent_co:
st.image(image=".streamlit/stock-market.png")
stocks = pd.read_excel("MCAP31122023.xlsx").set_index('Symbol')
url = "https://ticker.finology.in/company/"
model = LLM(model_name="Gemini")
# function with list of labels
def search_stocks(searchterm: str):
if not searchterm:
return []
matching_stocks = stocks[stocks.index.str.contains(searchterm, case=False, na=False)]
return matching_stocks.index.tolist()
selected_value = st_searchbox(
search_stocks,
key="wiki_searchbox",
)
if selected_value:
stock_url = f"https://ticker.finology.in/company/{selected_value}"
stock_fundamentals = generate_document(stock_url)
prompt = stock_analysis_prompt.replace(
"{stock_name}",selected_value).replace("{context}",stock_fundamentals.page_content)
result = model(prompt=prompt).replace('```',"")
try:
res = json.loads(result)
confidence_score = res['buy']
analysis = res["detailed_analysis"]
if confidence_score >= 75:
st.success("High Confidence Score!")
elif confidence_score > 40:
st.warning("Moderate Confidence Score.")
else:
st.error("Low Confidence Score.")
col1, col2 = st.columns(2)
col1.write(f'**Buy Confidence Score:** {str(confidence_score)}')
with st.expander("See explanation"):
st.write(f"**Detailed Analysis:** {analysis}")
st.markdown(f"[Learn more about {selected_value}]({stock_url})")
except:
st.write(result)
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