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# app.py
import gradio as gr
import torch
import pandas as pd
from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
import yfinance as yf
# Load the fine-tuned RAG model and tokenizer
tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
retriever = RagRetriever.from_pretrained("facebook/rag-sequence-base", index_name="custom")
model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever)
# Function to fetch and preprocess ICICI Bank data
def fetch_and_preprocess_data():
# Fetch ICICI Bank data using yfinance
ticker = "ICICIBANK.NS"
data = yf.download(ticker, start="2020-01-01", end="2023-01-01")
# Calculate technical indicators
data['MA_50'] = data['Close'].rolling(window=50).mean()
data['MA_200'] = data['Close'].rolling(window=200).mean()
return data
# Function to analyze trading data using the RAG model
def analyze_trading_data(question):
# Fetch and preprocess data
data = fetch_and_preprocess_data()
# Prepare context for the RAG model
context = (
f"ICICI Bank stock data:\n"
f"Latest Close Price: {data['Close'].iloc[-1]:.2f}\n"
f"50-Day Moving Average: {data['MA_50'].iloc[-1]:.2f}\n"
f"200-Day Moving Average: {data['MA_200'].iloc[-1]:.2f}\n"
)
# Combine question and context
input_text = f"Question: {question}\nContext: {context}"
# Tokenize the input
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
# Generate the answer using the RAG model
outputs = model.generate(inputs['input_ids'])
# Decode the output to get the answer
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
return answer
# Gradio interface
iface = gr.Interface(
fn=analyze_trading_data,
inputs="text",
outputs="text",
title="ICICI Bank Trading Analysis",
description="Ask any question about ICICI Bank's trading data and get a detailed analysis.",
examples=[
"What is the current trend of ICICI Bank stock?",
"Is the 50-day moving average above the 200-day moving average?",
"What is the latest closing price of ICICI Bank?"
]
)
# Launch the app
iface.launch() |