from sentence_transformers import SentenceTransformer
import torch
import gradio as gr
from huggingface_hub import InferenceClient
#theme of the UI
theme = gr.themes.Soft(
    primary_hue="green",
    secondary_hue="blue",
    neutral_hue="gray",
    font=["Roboto", "sans-serif"]
)

#semantic search application
with open("sustainability_tips.txt", "r", encoding="utf-8") as file:
    sustainability_tips_text = file.read()

def preprocess_text(text):
  cleaned_text = text.strip()
  chunks = cleaned_text.split("\n")
  cleaned_chunks = [chunk.strip() for chunk in chunks if chunk.strip() != '']
  return cleaned_chunks

cleaned_chunks = preprocess_text(sustainability_tips_text)

model = SentenceTransformer('all-MiniLM-L6-v2')

def create_embeddings(text_chunks):
  chunk_embeddings = model.encode(text_chunks, convert_to_tensor = True)
  return chunk_embeddings

chunk_embeddings = create_embeddings(cleaned_chunks)

def get_top_chunks(query, chunk_embeddings, text_chunks):
  query_embedding = model.encode(query, convert_to_tensor = True)
  query_embedding_normalized = query_embedding / query_embedding.norm()
  chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
  similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
  top_indices = torch.topk(similarities, k=3).indices
  top_chunks = [text_chunks[i] for i in top_indices]
  return top_chunks

client = InferenceClient("meta-llama/Llama-3.1-8B-Instruct")

# applying RAG
def respond(message, history):

    top_chunks = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
    context = "\n".join(top_chunks)

    messages = [{
    "role": "system",
    "content": (
        "You are EcoBot, a sustainability assistant.\n"
        "You help users reduce their environmental impact.\n\n"
        "You don't know anything about the current user."
        "Introduce yourself as EcoBot and share what you can do."

        "IMPORTANT RULES:\n"
        "- The context below is a list of sustainability tips from OTHER PEOPLE.\n"
        "- These are NOT things the user has done or mentioned.\n"
        "- Do NOT assume anything about the user's lifestyle.\n"
        "- Only use the context as suggestions or ideas to recommend.\n"
        "- If needed, ask a clarifying question before giving advice.\n\n"

        f"Context:\n{context}"
    )
}]

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

    response = ""

    stream = client.chat_completion(
        messages,
        max_tokens=250,
        top_p=0.2,
        stream=True
    )


    for event in stream:
        token = event.choices[0].delta.content
        if token:
            response += token
            yield response

#launching chatbot
#chatbot = gr.ChatInterface(
    #respond,
    #title="EcoBot 🌿",
    #description="Your sustainability assistant"
#).launch(theme=theme)

with gr.Blocks() as demo:
    chatbot = gr.Chatbot(placeholder="<strong>Hi, I'm EcoBot 🌱</strong><br>Ask me about living more sustainably")
    gr.ChatInterface(respond, chatbot=chatbot,title="EcoBot 🌿",description="Your sustainability assistant")
    
demo.launch(theme = theme)