Update app.py

app.py

@@ -8,7 +8,6 @@ import pandas as pd
 from sentence_transformers import SentenceTransformer
 from flask import Flask, request, jsonify, render_template
 from flask_cors import CORS
-from pyngrok import ngrok
 import requests
 import cloudinary
 import cloudinary.uploader
@@ -44,9 +43,9 @@ CLOUDINARY_API_KEY = os.getenv("CLOUDINARY_API_KEY", "default_key")
 CLOUDINARY_API_SECRET = os.getenv("CLOUDINARY_API_SECRET", "default_secret")
 
 # Define paths for models and data
-MODEL_PATH = os.path.join("models", "model_state_dict.pth")
-FAISS_INDEX_PATH = os.path.join("models", "property_faiss.index")
-DATASET_PATH = os.path.join("data", "property_data.csv")
+MODEL_PATH = os.path.join("models", "new_rag_model", "model_state_dict.pth")
+FAISS_INDEX_PATH = os.path.join("models", "new_rag_model", "property_faiss.index")
+DATASET_PATH = os.path.join("models", "new_rag_model", "property_data.csv")
 MODEL_DIR = os.path.join("models", "llm_model")
 
 # Check device
@@ -61,8 +60,8 @@ def load_sentence_transformer():
     print("Loading SentenceTransformer model...")
     try:
         model_embedding = SentenceTransformer("jinaai/jina-embeddings-v3", trust_remote_code=True).to(device)
-
-        # Load and optimize model state dict
+        if not os.path.exists(MODEL_PATH):
+            raise FileNotFoundError(f"Model state dict not found at {MODEL_PATH}. Please ensure the file exists.")
         state_dict = torch.load(MODEL_PATH, map_location=device)
 
         # Dequantize if needed
@@ -82,6 +81,8 @@ def load_sentence_transformer():
 # Load FAISS index
 def load_faiss_index():
     print("Loading FAISS index...")
+    if not os.path.exists(FAISS_INDEX_PATH):
+        raise FileNotFoundError(f"FAISS index not found at {FAISS_INDEX_PATH}. Please ensure the file exists.")
     index = faiss.read_index(FAISS_INDEX_PATH)
     print("FAISS index loaded successfully.")
     return index
@@ -89,6 +90,8 @@ def load_faiss_index():
 # Load dataset
 def load_dataset():
     print("Loading dataset...")
+    if not os.path.exists(DATASET_PATH):
+        raise FileNotFoundError(f"Dataset file not found at {DATASET_PATH}. Please ensure the file exists.")
     df = pd.read_csv(DATASET_PATH)
     print("Dataset loaded successfully.")
     return df