debugging

app.py

@@ -4,6 +4,7 @@ from huggingface_hub import InferenceClient
 # import lines go at the top: any libraries I need to import go up here ^^
 from sentence_transformers import SentenceTransformer
 import torch
+
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 # Step 1: Load the knowledge base
@@ -22,9 +23,6 @@ def preprocess_text(text):
 cleaned_chunks = preprocess_text(skincare_text)
 
 # Step 3: Convert chunks into embeddings
-from sentence_transformers import SentenceTransformer
-import torch
-
 model = SentenceTransformer('all-MiniLM-L6-v2')
 
 def create_embeddings(text_chunks):
@@ -46,7 +44,7 @@ def get_top_chunks(query, chunk_embeddings, text_chunks, top_k=3):
 # Step 5: Test the workflow with sample queries
 queries = [
     "Consistent skincare routine",
-    "Applying sunscreen daily",
+    "Applying sunscreen daily", 
     "Choosing products that match your skin type"
 ]
 
@@ -57,19 +55,16 @@ for q in queries:
         print(f"Result {idx}: {res}")
 
 def respond(message, history):
-
-    top_results = get_top_chunks(question, chunk_embeddings, cleaned_chunks)
+    top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
     print(top_results)
-
-    messages = [{"role": "system", "content": "You are a friendly chatbot. You give people advice about skincare. Base your response on the following information {top_results}"}]
-
-    if history: 
+    
+    messages = [{"role": "system", "content": f"You are a friendly chatbot. You give people advice about skincare. Base your response on the following information: {top_results}"}]
+    
+    if history:
         messages.extend(history)
-
     messages.append({"role": "user", "content": message})
-
-    response = client.chat_completion(messages, max_tokens = 100)
-
+    
+    response = client.chat_completion(messages, max_tokens=100)
     return response['choices'][0]['message']['content'].strip()
 
 def echo(message, history):