app.py

import gradio as gr
from huggingface_hub import InferenceClient

#STEP 1 FROM SEMATIC SEARCH
from sentence_transformers import SentenceTransformer
import torch

#STEP 2 FROM SEMATIC SEARCH
# Open the water_cycle.txt file in read mode with UTF-8 encoding
with open("water_cycle.txt", "r", encoding="utf-8") as file:
  # Read the entire contents of the file and store it in a variable
  water_cycle_text = file.read()
print(water_cycle_text)

#STEP 3 FROM SEMATIC SEARCH
def preprocess_text(text):
  # Strip extra whitespace from the beginning and the end of the text
  cleaned_text = text.strip()

  # Split the cleaned_text by every newline character (\n)
  chunks = cleaned_text.split("\n")

  # Create an empty list to store cleaned chunks
  cleaned_chunks = []

  # Write your for-in loop below to clean each chunk and add it to the cleaned_chunks list
  for chunk in chunks:
    chunk.strip()

    if chunk != "":
      cleaned_chunks.append(chunk)

  # Print cleaned_chunks
  print(cleaned_chunks)

  # Print the length of cleaned_chunks
  print(len(cleaned_chunks))

  # Return the cleaned_chunks
  return cleaned_chunks

# Call the preprocess_text function and store the result in a cleaned_chunks variable
cleaned_chunks = preprocess_text(water_cycle_text) # Complete this line

#STEP 4 FROM SEMATIC SEARCH
# Load the pre-trained embedding model that converts text to vectors
model = SentenceTransformer('all-MiniLM-L6-v2')

def create_embeddings(text_chunks):
  # Convert each text chunk into a vector embedding and store as a tensor
  chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True) # Replace ... with the text_chunks list

  # Print the chunk embeddings
  print(chunk_embeddings)

  # Print the shape of chunk_embeddings
  print(chunk_embeddings.shape)

  # Return the chunk_embeddings
  return chunk_embeddings

# 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 FROM SEMATIC SEARCH
# Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks
def get_top_chunks(query, chunk_embeddings, text_chunks):
  # Convert the query text into a vector embedding
  query_embedding = model.encode(query, convert_to_tensor = True) # Complete this line

  # Normalize the query embedding to unit length for accurate similarity comparison
  query_embedding_normalized = query_embedding / query_embedding.norm()

  # Normalize all chunk embeddings to unit length for consistent comparison
  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)

  # 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 = []

  # Loop through the top indices and retrieve the corresponding text chunks
  for top_index in top_indices:
    top_chunks.append(text_chunks[top_index])

  # Return the list of most relevant chunks
  return top_chunks

#STEP 6 FROM SEMATIC SEARCH
# Call the get_top_chunks function with the original query
top_results = get_top_chunks("How do you make banana bread?", chunk_embeddings, cleaned_chunks)

# Print the top results
print(top_results)


client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")

def respond(message, history):
    top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
    print(top_results)
    str_top_results = "\n".join(top_results)
    
    messages = [{"role": "system", "content": f"You're a friendly and gen z chatbot. Base your response on the provided context: {top_results}."}]

    if history:
        messages.extend(history)

    messages.append({"role": "user", "content": message})

    response = client.chat_completion(
        messages,
        max_tokens =  1000,
        temperature = 1
    )
    
    return response['choices'][0]['message']['content'].strip()
    
chatbot = gr.ChatInterface(respond, type = 'messages')
chatbot.launch(debug = True)