Update app.py

app.py

@@ -1,13 +1,24 @@
 import streamlit as st
+import pandas as pd
 from PIL import Image
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification
 import torch
-from datetime import datetime
 import openai
 
 # Initialize OpenAI API key
 openai.api_key = st.secrets["GPT_TOKEN"]
 
+# Dataset loading function with caching
+@st.cache_data
+def load_datasets():
+    try:
+        with st.spinner('Loading dataset...'):
+            original_data = pd.read_csv('CTP_Model1.csv', low_memory=False)
+            return original_data
+    except Exception as e:
+        st.error(f"Error loading dataset: {str(e)}")
+        raise e
+
 # Function to classify the car image using pre-trained model
 def classify_image(image):
     try:
@@ -51,6 +62,9 @@ def get_car_overview(brand, model, year):
 st.title("Auto Appraise")
 st.write("Upload a car image or take a picture to get its brand, model, and overview!")
 
+# Load dataset
+dataset = load_datasets()
+
 # Get the session state
 if 'image' not in st.session_state:
     st.session_state.image = None
@@ -105,6 +119,14 @@ if st.session_state.image is not None:
         st.write("Car Overview:")
         st.write(overview)
 
+        # Match the car make and model to a record in the dataset
+        matched_entry = dataset[(dataset['Make'] == make_name) & (dataset['model'] == model_name)]
+
+        if not matched_entry.empty:
+            st.write("Matched Entry from Dataset:")
+            st.write(matched_entry)
+        else:
+            st.write("No matching entry found in the dataset.")
     else:
         st.error("Could not classify the image. Please try again with a different image.")
 else: