app.py

import gradio as gr
import random
# import os
from huggingface_hub import InferenceClient
from sentence_transformers import SentenceTransformer
import torch

with open("knowledge.txt", "r", encoding="utf-8") as file:
  recent = file.read()
  # opens the text, saves as "file"
  # reads the text and saves as water_cycle_text variable



cleaned_text = recent.strip()
# cleaning up the text
chunks = cleaned_text.split("\n")
# seperating the text into one sentence pieces
cleaned_chunks = []
# creating an empty list to put the cleaned chunks in

for chunk in chunks:
  stripped_chunk = chunk.strip()
  if stripped_chunk:
    cleaned_chunks.append(stripped_chunk)
    # loop through chunks and add not empty chunks to cleaned_chunks list


model = SentenceTransformer('all-MiniLM-L6-v2')

chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
# encode the model, pass through my cleaned chunks and convert to vector embeddings (not arrays)


def get_top_chunks(query):
  # create my function taking query as parameter
  query_embedding = model.encode(query, convert_to_tensor=True)
  # encode query to vector embedding for comparison
  query_embedding_normalized = query_embedding / query_embedding.norm()
  # normalize my query to 1; allows for comparison of meaning
  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
  # normailizing chunks for comparison of meaning

  similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)

  # using matmul (matrix multiplication method) to compare query to chunks
  top_indices = torch.topk(similarities, k=3).indices

  # get the indices of the chunks that are most similar to query

  top_chunks = []

  for i in top_indices:
    chunk = chunks[i]
    # for each index number in top_indices, get back the text
    top_chunks.append(chunk)
    # values of each index number is added to top_chunks
  return top_chunks
    
client = InferenceClient('google/gemma-3-27b-it',)
    #token = 'HF_TOKEN'

#client is where you can change the LLM model!
def respond(message,history):
    #if not message.strip():
        #return "Hello!"
    gift_ideas = get_top_chunks(message)
    messages = [{'role': 'system', 'content': f'You give really good gift ideas and are super helpful! You also tell me the price of each item. Give me 5 gift ideas if I ask. Use the following database for gift ideas: {gift_ideas}'}]
    
    if history:
        messages.extend(history)
    
    messages.append({"role": "user", "content": message})

    response = client.chat_completion(
    # for message in client.chat_completion(
        #max_tokens controls how many words your responses is 
        messages,
        max_tokens = 500,
    )
        # stream = True,
        
        #temperature = 0.8, #code a decimal between 0-2
        #top_p = .65 #code a decimal between 0-1
    #):
        # token = message.choices[0].delta.content
        # response += token
    return response['choices'][0]['message']['content'].strip()
    #yield response
    #print(response["choices"][0]["message"]["content"].strip())
    
    #yield response["choices"][0]["message"]["content"].strip()
    


#with gr.Blocks(theme='hmb/amethyst') as demo:
 #   with gr.Row(equal_height=True):
  #      with gr.Column(scale=10):
   #            """
    #            # 🎁 Introducing WrapIT!
     #           **WrapIT** helps users find personalized gift ideas and craft thoughtful card messages  
      #          by inputting details like the recipient's interests, celebration type, and budget.
       #         
        #        ✨ *All you have to do is wrap it.*
         #       """
          #  )
           # gr.ChatInterface(respond, type='messages')

  
    
    #chatbot = gr.Chatbot()
    #msg = gr.Textbox(placeholder="Say hi to WrapIT here!", label="Message")
    #send = gr.Button("Send")

    #msg.submit(respond, [msg, chatbot], [msg, chatbot])
    #send.click(respond, [msg, chatbot], [msg, chatbot])
print("The bug is in the gradio")
with gr.Blocks(theme='hmb/amethyst') as demo:
    # Top image
    gr.Image(value="wrap_it_top_image.png", show_label=False, elem_id="top-image")

    # Title and description
    gr.Markdown("## 🎁 Introducing WrapIT!")
    gr.Markdown("**WrapIT** helps users find personalized gift ideas and craft thoughtful card messages by inputting details like the recipient's interests, celebration type, and budget ✨ *All you have to do is wrap it.*")

    # Chat interface
    
    gr.ChatInterface(
        fn=respond,
        examples=["Best birthday gift?", "Romantic anniversary idea?", "Budget-friendly gifts?"]
    )


    with gr.Row():
        gr.HTML(
            """
            <iframe style="border-radius:12px"
                    src="https://open.spotify.com/embed/track/4356Typ82hUiFAynbLYbPn"
                    width="100%"
                    height="152"
                    frameBorder="0"
                    allowfullscreen=""
                    allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture"
                    loading="lazy">
            </iframe>
            """
        )

demo.launch(debug=True, share=True)