app.py

import gradio as gr
import os
from huggingface_hub import InferenceClient #imports huggingface models
from sentence_transformers import SentenceTransformer
import torch
import numpy as np


# Load and process the knowledge base text file
with open("knowledge.txt", "r", encoding="utf-8") as f:
    knowledge_text = f.read()


# Split the text into chunks (for example, by paragraphs)
chunks = [chunk.strip() for chunk in knowledge_text.split("\n\n") if chunk.strip()]


# Load an embedding model (this one is light and fast)
embedder = SentenceTransformer('all-MiniLM-L6-v2')


# Precompute embeddings for all chunks (as a tensor for fast similarity search)
chunk_embeddings = embedder.encode(chunks, convert_to_tensor=True)
def get_relevant_context(query, top_k=3):
    """
     Compute the embedding for the query, compare it against all chunk embeddings,
     and return the top_k most similar chunks concatenated into a context string.
    """
    # Compute and normalize the query embedding
    query_embedding = embedder.encode(query, convert_to_tensor=True)
    query_embedding = query_embedding / query_embedding.norm()
    
    # Normalize chunk embeddings along the embedding dimension
    norm_chunk_embeddings = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
    
    # Compute cosine similarity between the query and each chunk
    similarities = torch.matmul(norm_chunk_embeddings, query_embedding)
    
    # Get the indices of the top_k most similar chunks
    top_k_indices = torch.topk(similarities, k=top_k).indices.cpu().numpy()
    
    # Concatenate the top chunks into a single context string
    context = "\n\n".join([chunks[i] for i in top_k_indices])
    return context


custom_theme = gr.themes.Soft(
    primary_hue="green",
    secondary_hue="stone",
    neutral_hue="gray",
    spacing_size="md",
    radius_size="md",
    text_size="md",
    font=["Roboto", "sans-serif"],
    font_mono=["Roboto Mono", "monospace"],
)



client = InferenceClient("google/gemma-2-2b-it")


def respond(message, history):
    messages = [{"role": "system", "content": "You are ChaChingas, an AI financial advisor for students and low-income families. Only answer questions about budgeting, saving, debt, credit card fraud, investing, and finance. If a user asks about unrelated topics like recipes, sports, or entertainment, politely say: 'I'm here to help with money and budgeting—ask me anything about that!' Speak clearly, keep answers short, and use simple language. When asked about budgeting, explain the 50/30/20 rule: 50% for needs, 30% for wants, 20% for savings or debt. Be supportive, practical, and easy to understand. Avoid giving tax or legal advice, and never ask for or handle sensitive personal financial information."}]
    context = get_relevant_context(message, top_k=3)
    
# add all previous messages to the messages list
    if history:
        for user_msg, assistant_msg in history:
            messages.append({"role": "user", "content": user_msg})
            messages.append({"role": "assistant", "content": assistant_msg})

            
    # add the current user's message to the messages list
    messages.append({"role": "user", "content": message})

    
    # makes the chat completion API call,
    # sending the messages and other parameters to the model
    # implements streaming, where one word/token appears at a time
    
    response = ""

    
    # iterate through each message in the method
    for message in client.chat_completion(
        messages,
        max_tokens=500,
        temperature=.1,
        stream=True):
        # add the tokens to the output content
          token = message.choices[0].delta.content # capture the most recent toke
          response += token # Add it to the response
          yield response # yield the response:

with gr.Blocks(theme=custom_theme) as demo:
    #gr.Image(
        #value="Banner.png",
        #show_label=False,
        #show_share_button=False,
        #show_download_button=False
    #)

    chatbot_interface = gr.ChatInterface(
        respond,
        examples=["Build a Budgeting Plan","Teach Me About Stocks","How Do I Set Up a Bank Account?", "How Do I Prevent Credit Card Fraud?"],
        title="ChaChingas",
        description="This is a financial literacy chatbot"
    )


demo.launch()

#build on your original chatbot from the previous lesson
#a basic chatbot from the previous lesson is below -- edit it to incorporate the changes described above