Update app.py

app.py

@@ -1,43 +1,51 @@
-from sentence_transformers import SentenceTransformer, util
-import gradio as gr
-import torch
-
-# Load a pre-trained sentence-transformer model
-model = SentenceTransformer('all-MiniLM-L6-v2')
-
-# Define your dataset
-conversations = [
-    {"user": "What are your store hours?", "bot": "Our store is open from 9 AM to 9 PM, Monday to Saturday."},
-    {"user": "Do you sell laptops?", "bot": "Yes, we offer a range of laptops from brands like Dell, HP, and Lenovo."},
-    {"user": "What is the price of the iPhone 14?", "bot": "The iPhone 14 starts at $799."},
-    {"user": "Can I return a product I bought last week?", "bot": "You can return products within 30 days of purchase with a valid receipt."},
-    {"user": "Do you have any discounts available?", "bot": "Yes, we currently have a 10% discount on selected electronics."},
-    {"user": "What is your exchange policy?", "bot": "You can exchange items within 14 days of purchase, as long as they are in original condition with a receipt."},
-    {"user": "How can I track my order?", "bot": "You can track your order by logging into your account and clicking 'Track Order' under 'My Orders'."},
-    {"user": "Do you offer home delivery?", "bot": "Yes, we offer home delivery for most items. Delivery charges may apply based on your location."},
-    {"user": "Can I cancel my order?", "bot": "Yes, you can cancel your order within 24 hours of placing it by going to your account and selecting the cancel option."},
-    {"user": "Do you have any new arrivals in smartphones?", "bot": "Yes, we have the latest models from Apple, Samsung, and OnePlus available in store and online."}
-]
-
-# Precompute embeddings for the dataset
-conversation_texts = [conv['user'] for conv in conversations]
-conversation_embeddings = model.encode(conversation_texts, convert_to_tensor=True)
-
-def chatbot_response(user_input):
-    # Compute embedding for the user input
-    user_embedding = model.encode(user_input, convert_to_tensor=True)
-    
-    # Compute cosine similarity between the user input and all predefined conversations
-    similarities = util.pytorch_cos_sim(user_embedding, conversation_embeddings)
-    
-    # Find the conversation with the highest similarity
-    best_match_idx = torch.argmax(similarities)
-    
-    # Return the bot response from the best matching conversation
-    return conversations[best_match_idx]['bot']
-
-# Create Gradio interface
-iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text", title="Retail Store Chatbot")
-
-# Launch the chatbot
-iface.launch()
+from sentence_transformers import SentenceTransformer, util
+import gradio as gr
+import torch
+
+# Load a pre-trained sentence-transformer model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Define your dataset
+conversations = [
+    {"user": "What are your store hours?", "bot": "Our store is open from 9 AM to 9 PM, Monday to Saturday."},
+    {"user": "Do you sell laptops?", "bot": "Yes, we offer a range of laptops from brands like Dell, HP, and Lenovo."},
+    {"user": "What is the price of the iPhone 14?", "bot": "The iPhone 14 starts at $799."},
+    {"user": "Can I return a product I bought last week?", "bot": "You can return products within 30 days of purchase with a valid receipt."},
+    {"user": "Do you have any discounts available?", "bot": "Yes, we currently have a 10% discount on selected electronics."},
+    {"user": "What is your exchange policy?", "bot": "You can exchange items within 14 days of purchase, as long as they are in original condition with a receipt."},
+    {"user": "How can I track my order?", "bot": "You can track your order by logging into your account and clicking 'Track Order' under 'My Orders'."},
+    {"user": "Do you offer home delivery?", "bot": "Yes, we offer home delivery for most items. Delivery charges may apply based on your location."},
+    {"user": "Can I cancel my order?", "bot": "Yes, you can cancel your order within 24 hours of placing it by going to your account and selecting the cancel option."},
+    {"user": "Do you have any new arrivals in smartphones?", "bot": "Yes, we have the latest models from Apple, Samsung, and OnePlus available in store and online."}
+]
+
+# Precompute embeddings for the dataset
+conversation_texts = [conv['user'] for conv in conversations]
+conversation_embeddings = model.encode(conversation_texts, convert_to_tensor=True)
+
+def chatbot_response(user_input):
+    # Compute embedding for the user input
+    user_embedding = model.encode(user_input, convert_to_tensor=True)
+    
+    # Compute cosine similarity between the user input and all predefined conversations
+    similarities = util.pytorch_cos_sim(user_embedding, conversation_embeddings)
+    
+    # Find the conversation with the highest similarity
+    best_match_idx = torch.argmax(similarities)
+    
+    # Return the bot response from the best matching conversation
+    return conversations[best_match_idx]['bot']
+
+# Create Gradio interface
+iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text", title="Retail Store Chatbot",description="Ask me anything about our retail store! I can provide information about store hours, product availability, return policies, and more.",
+    examples=[
+        ["What are your store hours?"],
+        ["Do you sell laptops?"],
+        ["What is the price of the iPhone 14?"],
+        ["Can I return a product I bought last week?"],
+        ["Do you have any discounts available?"]
+    ]
+)
+
+# Launch the chatbot
+iface.launch()