from huggingface_hub import InferenceClient 
#step 1 from semantic search
from sentence_transformers import SentenceTransformer
import torch

import gradio as gr
import random

client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")
#step 2 from semantic search read file

# Open the water_cycle.txt file in read mode with UTF-8 encoding
with open("reconext_file.txt", "r", encoding="utf-8") as file:
  # Read the entire contents of the file and store it in a variable
  reconext_file_text = file.read()
    
# Print the text below
print(reconext_file_text)

#step 3 from semantix 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:
    clean_chunk = chunk.strip()
    if(len(clean_chunk) >= 0):
      cleaned_chunks.append(clean_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(reconext_file_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 chunks in top_indices:
    top_chunks.append(chunks)

  # Return the list of most relevant chunks
  return top_chunks



def respond(message, history):
    best_next_watch = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
    print(best_next_watch)
    str_watch_chunks = "\n".join(best_next_watch)
    messages = [
        {"role":"system", 
        "content": "You are a gen-z helpful chatbot that helps teenagers find their next best watch, speak in gen-z terms and be natural. You should answer the users question based on " + str_watch_chunks + " ."
        }
    ]
    if history:
        messages.extend(history)

    messages.append(
        {'role':'user', 
        'content':message}
    )
    
    response = client.chat_completion(
        messages, max_tokens = 300, temperature=1.3, top_p=0.6
    )
    return response['choices'][0]['message']['content'].strip()

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