Update app.py

app.py

@@ -27,7 +27,7 @@ with open("financial_aid.txt", "r", encoding="utf-8") as file:
     acadenic_tips_text = file.read()
 
 # Print the text below
-print(poverty_and_education)
+print(academic_tips_text)
 
 # ===== APPLY THE COMPLETE WORKFLOW =====
 
@@ -117,16 +117,16 @@ def get_top_chunks(query, chunk_embeddings, text_chunks):
 
 # Print the top results
 #print(top_results)
-cleaned_chunks = preprocess_text(poverty_and_education)
+cleaned_chunks = preprocess_text(academic_tips_text)
 chunk_embeddings = create_embeddings(cleaned_chunks)
 #AI API being used
-client= InferenceClient("Qwen/Qwen2.5-7B-Instruct-1M")
+client= InferenceClient("all-MiniLM-L6-v2")
 response=""
 #defining role of AI and user
 
 def respond(message,history):
     information = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
-    messages = [{"role": "assistant", "content": f"You are a friendly chatbot that gives advice to disadvantaged students about their education based on their question. When you give advice, keep in mind the following infromation {information}"}]
+    messages = [{"role": "assistant", "content": f"You are a friendly, helpful chatbot that gives academic advice to disadvantaged students about their education based on their question. When you give advice, keep in mind the following infromation {information}"}]
 
 
     if history:
@@ -140,7 +140,7 @@ def respond(message,history):
 
 ### STEP 6
 # Call the preprocess_text function and store the result in a cleaned_chunks variable
-cleaned_chunks = preprocess_text(poverty_and_education) # Complete this line
+cleaned_chunks = preprocess_text(academic_tips_text) # Complete this line
 top_results = get_top_chunks("How does poverty affect one's education?", chunk_embeddings, cleaned_chunks) # Complete this line
 
 print(top_results)