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| import gradio as gr | |
| import torch | |
| import pandas as pd | |
| import yfinance as yf | |
| from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration | |
| # Check if GPU is available | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| print(f"Using device: {device}") | |
| # Function to fetch and preprocess ICICI Bank data | |
| def fetch_and_preprocess_data(): | |
| try: | |
| ticker = "ICICIBANK.BO" # ICICI Bank BSE Symbol | |
| data = yf.download(ticker, start="2020-01-01", end="2023-01-01") | |
| if data.empty: | |
| raise ValueError("No data found for the given symbol.") | |
| # Calculate Moving Averages | |
| data['MA_50'] = data['Close'].rolling(window=50).mean() | |
| data['MA_200'] = data['Close'].rolling(window=200).mean() | |
| return data | |
| except Exception as e: | |
| print(f"Error fetching data: {e}") | |
| return pd.DataFrame() # Return empty DataFrame if fetching fails | |
| # Load the RAG model and tokenizer with error handling | |
| try: | |
| tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base") | |
| print("Tokenizer loaded successfully.") | |
| retriever = RagRetriever.from_pretrained( | |
| "facebook/rag-sequence-base", | |
| index_name="wiki_dpr", | |
| passages_path=None, | |
| index_path=None | |
| ) | |
| print("Retriever loaded successfully.") | |
| model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever).to(device) | |
| print("Model loaded successfully.") | |
| except Exception as e: | |
| print(f"Error loading model or retriever: {e}") | |
| retriever = None | |
| model = None | |
| # Function to analyze trading data | |
| def analyze_trading_data(question): | |
| if model is None or retriever is None: | |
| return "Error: Model or retriever is not initialized. Please check the logs." | |
| # Fetch and preprocess data | |
| data = fetch_and_preprocess_data() | |
| if data.empty: | |
| return "Error: No data available for analysis." | |
| # Prepare context for 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 input | |
| inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True).to(device) | |
| # Generate answer using the model | |
| outputs = model.generate(inputs['input_ids']) | |
| # Decode output | |
| 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 | |
| if __name__ == "__main__": | |
| iface.launch() | |