app.py

import gradio as gr
from huggingface_hub import InferenceClient

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

#STEP 2 FROM SEMANTIC 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 the text below
print(water_cycle_text)

# Print the text below
print(water_cycle_text)


#STEP 3 FROM SEMANTIC 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:
    cleaned_chunk = chunk.strip()
    if len(cleaned_chunk) > 0:
      cleaned_chunks.append(cleaned_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 SEMANTIC 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 SEMANTIC 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 index in top_indices:
    relevant_chunk = text_chunks[index]
    top_chunks.append(relevant_chunk)

  # Return the list of most relevant chunks
  return top_chunks

#STEP 6 FROM SEMANTIC SEARCH
# Call the get_top_chunks function with the original query
top_results = get_top_chunks("How does water get into the sky?", chunk_embedings, cleaned_chunks) # Complete this line

# Print the top results
print(top_results)


client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(message, history):
    messages = [{"role": "system", "content": "You are a friendly chatbot"}]
    
    if history:
        messages.extend(history) 

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

    response = client.chat_completion (
        messages,
        max_tokens=100,
        temperature=0.2
    )
    
    return response['choices'][0]['message']['content'].strip()

chatbot = gr.ChatInterface(respond, type="messages")
chatbot.launch()