Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import random
 from huggingface_hub import InferenceClient
 
-#STEP 1: Import Sentence Transformer Library And Torch
 from sentence_transformers import SentenceTransformer
 import torch
 
@@ -17,9 +16,9 @@ charities_text = load_files("charities.txt")
 financial_advice_text = load_files("financial_advice.txt")
 
 
-# ===== APPLY THE COMPLETE WORKFLOW =====
+# 
 
-### STEP 3
+### 
 def preprocess_text(text):
     # Strip extra whitespace from the beginning and the end of the text
     cleaned_text = text.strip()
@@ -36,15 +35,12 @@ def preprocess_text(text):
         if len(stripped_chunk) > 0:
             cleaned_chunks.append(stripped_chunk)
 
-    # Print cleaned_chunks
-    #print(cleaned_chunks)
-
+    
     # Print the length of cleaned_chunks
     num_of_chunks = len(cleaned_chunks)
    # print(num_of_chunks)
 
-   # print(f"There are {num_of_chunks} amount of chunks")
-    # Return the cleaned_chunks
+ 
     return cleaned_chunks
 
 cleaned_charities = preprocess_text(charities_text)
@@ -69,8 +65,6 @@ def create_embeddings(text_chunks):
 
 charity_embeddings = create_embeddings(cleaned_charities)
 finance_embeddings = create_embeddings(cleaned_finance)
-# Call the create_embeddings function and store the result in a new chunk_embeddings variable
-#chunk_embeddings = create_embeddings(cleaned_chunks) # Complete this line
 
 ###STEP 5
 # Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks
@@ -87,15 +81,11 @@ def get_top_chunks(query, chunk_embeddings, text_chunks):
     # Calculate cosine similarity between query and all chunks using matrix multiplication
     similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) # Complete this line
 
-    # Print the similarities
-    #print(similarities)
+   
 
     # Find the indices of the 3 chunks with highest similarity scores
     top_indices = torch.topk(similarities, k=3).indices
 
-    # Print the top indices
-    #print(top_indices)
-
     # Create an empty list to store the most relevant chunks
     top_chunks = []
 
@@ -139,38 +129,12 @@ def respond(message, history, chatbot_topic_values, chatbot_mode_values):
     response = client.chat_completion(messages, temperature=0.2)
     return response['choices'][0]['message']['content'].strip()
 
-#def respond(message,history):
- #   topic_chunks = []
-  #  if chatbot_topic and "Helping Charities" in chatbot_topic:
-   #     topic_chunks = get_top_chunks(message, charity_embeddings, cleaned_charities)
-    #elif chatbot_topic and "Financial Aid" in chatbot_topic:
-     #   topic_chunks = get_top_chunks(message, finance_embeddings, cleaned_finance)
-    
-    
-    #return information
-    #return topic_chunks
-    #chatbot_mode=="Advice Mode"
-    #messages = [{"role": "assistant", "content": f"You are a helpful and insightful chatbot who acts like a financial advisor of a university student who wants to learn to manage their personal finances. You analyse their situation and give relevant advice and insights. You only answer in complete sentences with correct grammar, punctuation, and complete ideas. You respond clearly in under five complete bullet points under 500 characters. When you give advice, keep in mind the following information {topic_chunks}"}]
-
-#    if chatbot_mode == "Advice Mode":
- #       role_message = ( "You are a helpful and insightful chatbot who acts like a financial " "advisor of a university student. Respond in under five bullet points, " f"under 500 characters, using this context: {topic_chunks}" ) 
-  #  else: role_message = f"You are a helpful chatbot. Use this context: {topic_chunks}"
-#
- #   if history:
-  #      messages.extend(history) #keep adding history 
-        
-   # messages.append({"role":"user","content": message})
-
-    #response=client.chat_completion(messages, temperature=0.2)#capping how many words the LLM is allowed to generate as a respond (100 words)
-    
-    #return response['choices'][0]['message']['content'].strip() #storing value of response in a readable format to display
 
 ### STEP 6
 # Call the preprocess_text function and store the result in a cleaned_chunks variable
 cleaned_chunks = preprocess_text(financial_advice_text) # Complete this line
 top_results = get_top_chunks("What financial advice you give me?", finance_embeddings, cleaned_finance) 
 
-#print(top_results)
 
 #Defining chatbot giving user a UI to interact, see their conversation history, and see new messages using built in gr feature
 #ChatInterface requires at least one parameter(a function)
@@ -200,10 +164,11 @@ def save_chat_history(history, username):
 
 with gr.Blocks(
     theme=gr.themes.Soft(
-        primary_hue="purple",
+        primary_hue="blue",
         secondary_hue="fuchsia",
         neutral_hue="gray",
-        text_size="lg"
+        text_size="lg",
+        text_font="Roboto"
     ).set(
         background_fill_primary='*neutral_200',
         background_fill_secondary='neutral_100',
@@ -225,20 +190,6 @@ with gr.Blocks(
         with gr.Row(scale=1):
             chatbot_mode=gr.CheckboxGroup(["Advice Mode"], label="How would you like the chatbot to respond?")
 
-        #with gr.Row():
-            #save_button = gr.Button("💾 Save Chat History", 
-                                  #variant="primary",
-                                  #size="sm")
-            #download_button = gr.File(interactive=True, 
-                                    #visible=True,
-                                    #elem_classes=["download-btn"])
-            
-       # gr.ChatInterface(
-        #    fn=lambda msg, hist: respond(msg, hist, chatbot_topic.value, chatbot_mode.value),
-         #   title="Finance Management Hub",
-          #  description="Ask about your personal finance",
-           # type="messages",
-    #)
         gr.ChatInterface(
             fn=lambda msg, hist, topic_vals, mode_vals: respond(msg, hist, topic_vals, mode_vals),
             title="Finance Management Hub",