Update app.py

app.py

@@ -2,28 +2,38 @@
 import gradio as gr
 import torch
 import pandas as pd
-from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration, RagConfig
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
 from datasets import Dataset
 import yfinance as yf
 import numpy as np
 
 # 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
+    try:
+        # Fetch ICICI Bank data using yfinance
+        ticker = "ICICIBANK.BO"  # Use 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 technical indicators
+        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 an empty DataFrame if fetching fails
 
 # Function to create and save a custom index for the retriever
 def create_custom_index():
     # Fetch and preprocess data
     data = fetch_and_preprocess_data()
     
+    if data.empty:
+        raise ValueError("No data available to create the index.")
+    
     # Create a dataset for the retriever
     dataset = Dataset.from_dict({
         "id": [str(i) for i in range(len(data))],
@@ -43,25 +53,36 @@ def create_custom_index():
 # Load the fine-tuned RAG model and tokenizer
 tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-base")
 
-# Create and save the custom index
-dataset_path, index_path = create_custom_index()
-
-# Load the retriever with the custom index
-retriever = RagRetriever.from_pretrained(
-    "facebook/rag-sequence-base",
-    index_name="custom",
-    passages_path=dataset_path,
-    index_path=index_path
-)
-
-# Load the RAG model
-model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever)
+try:
+    # Create and save the custom index
+    dataset_path, index_path = create_custom_index()
+    
+    # Load the retriever with the custom index
+    retriever = RagRetriever.from_pretrained(
+        "facebook/rag-sequence-base",
+        index_name="custom",
+        passages_path=dataset_path,
+        index_path=index_path
+    )
+    
+    # Load the RAG model
+    model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-base", retriever=retriever)
+except Exception as e:
+    print(f"Error initializing model or retriever: {e}")
+    retriever = None
+    model = None
 
 # Function to analyze trading data using the RAG model
 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 the RAG model
     context = (
         f"ICICI Bank stock data:\n"