Update electronics_query.py

electronics_query.py

@@ -1,5 +1,21 @@
+import os
 import pandas as pd
 import random
+from transformers import DistilBertTokenizer, DistilBertModel
+from huggingface_hub import login
+
+# Check if the token is available
+HUGGINGFACE_TOKEN = os.getenv('HUGGINGFACE_TOKEN')
+if not HUGGINGFACE_TOKEN:
+    raise EnvironmentError("HUGGINGFACE_TOKEN environment variable is not set.")
+
+# Log in to Hugging Face
+login(token=HUGGINGFACE_TOKEN)
+
+# Load model and tokenizer
+model_name = "distilbert-base-uncased"
+tokenizer = DistilBertTokenizer.from_pretrained(model_name, use_auth_token=HUGGINGFACE_TOKEN)
+model = DistilBertModel.from_pretrained(model_name, use_auth_token=HUGGINGFACE_TOKEN)
 
 # Load the electronics dataset
 def load_electronics_dataset():
@@ -26,7 +42,7 @@ electronics_response_templates = [
     # Add more templates as needed
 ]
 
-# Generate a dynamic response for electronics
+# Generate a dynamic response
 def generate_electronics_response(row):
     template = random.choice(electronics_response_templates)
     return template(row)
@@ -69,8 +85,9 @@ def query_electronics(user_query, n_results=5):
     # Return the top N results
     return sorted_df.head(n_results)
 
-# Function to integrate the chatbot responses for electronics
+# Function to integrate the chatbot responses
 def electronics_chatbot_response(user_query):
+    # Query the electronics data
     electronics_results = query_electronics(user_query)
 
     if not electronics_results.empty:
@@ -79,4 +96,5 @@ def electronics_chatbot_response(user_query):
             response += generate_electronics_response(row) + "\n"
         return response
 
-    return "Sorry, I couldn't find any electronics matching your query."
+    # If no results, generate a fallback response
+    return "Sorry, I couldn't find any electronics matching your query."