app.py

import gradio as gr
from huggingface_hub import InferenceClient
from sentence_transformers import SentenceTransformer
import torch

import random

#Semantic Search
#STEP 1
#!pip install -q sentence-transformers


#STEP 2
# 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)

#STEP 3
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:
    stripped_chunk = chunk.strip()
    if len(stripped_chunk) >= 0:
      cleaned_chunks.append(stripped_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)

#STEP 4
# 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) # no parentheses on .shape because it's a property, not a method! Look up the difference between class methods and classes properties.

  # 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)

#STEP 5
# 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)

  # 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)

  # 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:
    chunk = text_chunks[index]
    top_chunks.append(chunk)

  # Return the list of most relevant chunks
  return top_chunks

#STEP 6 or Practice
# ===== LOAD & PROCESS YOUR NEW CONTENT =====
with open("em_spectrum.txt", "r", encoding="utf-8") as file:
  # Read the entire contents of the file and store it in a variable
  em_spectrum_text = file.read()

# Print the text below
print(em_spectrum_text)

# ===== APPLY THE COMPLETE WORKFLOW =====
#need cleaned_chunks variable
#need chunk_embeddings variable
em_cleaned_chunks = preprocess_text(em_spectrum_text)
em_chunk_embeddings = create_embeddings(em_cleaned_chunks)

test_question = "What type of EM radiation has the most energy?"

print("test question:", test_question)
em_top_results = get_top_chunks(test_question, em_chunk_embeddings, em_cleaned_chunks)
print(em_top_results)


# ===== EXPERIMENT & VERIFY =====

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)
    print(response)
    return response['choices'][0]['message']['content'].strip()
    
chatbot = gr.ChatInterface(respond, type="messages")
chatbot.launch()

print(messages)
print(history)