app.py

import gradio as gr
import requests
from huggingface_hub import InferenceClient
#----------------------------------------------------------------------------------------------------------------------------------------------

# Step 1 - Semantic Search
from sentence_transformers import SentenceTransformer
import torch



# Step 2 - 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)



# Step 3 - 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:
    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



# Step 4 - 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

    # 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 5 - Semantic Search
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 i in top_indices:
    chunk = text_chunks[i]
    top_chunks.append(chunk)

  # Return the list of most relevant chunks
  return top_chunks



# Step 6 - 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_embeddings, cleaned_chunks) # Complete this line
    # Print the top results
print(top_results)

#--------------------------------------------------------------------------------------------------------------------------------------------
SPOONACULAR_API_KEY = "71259036cfb3405aa5d49c1220a988c5"

def get_recipes(ingredient):
    url = "https://api.spoonacular.com/recipes/complexSearch"
    params = {
        "query": ingredient,
        "number": 3,
        "apiKey": SPOONACULAR_API_KEY
    }
    res = requests.get(url, params=params)
    data = res.json()
    return [r["title"] for r in data["results"]]

iface = gr.Interface(
    fn=get_recipes,
    inputs="text",
    outputs="text",
    title="Spoonacular Recipe Finder"
)

iface.launch()

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


if __name__ == "__main__":
    demo.launch()