import gradio as gr

theme = gr.themes.Soft(
    primary_hue="pink",
    secondary_hue="pink",
    neutral_hue="fuchsia",
).set(
    link_text_color='*secondary_700'
)


from huggingface_hub import InferenceClient
from sentence_transformers import SentenceTransformer
import torch
import numpy as np

with open("knowledge.txt" , "r", encoding="utf-8") as f:
    knowledge_base = f.read()

print("Knowledge base loaded.")

cleaned_text = knowledge_base.strip()

chunks = cleaned_text.split("\n")
cleaned_chunks = []

for chunk in chunks:
  stripped_chunk = chunk.strip()
  if stripped_chunk:
    cleaned_chunks.append(stripped_chunk)
print(cleaned_chunks)

model = SentenceTransformer('all-MiniLM-L6-v2')
chunk_embeddings = model.encode(cleaned_chunks, convert_to_tensor=True)
print(chunk_embeddings)

def get_top_chunks(query):
  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)
  print(similarities)

  top_indices = torch.topk(similarities, k=3).indices
  print(top_indices)

  top_chunks = []

  for i in top_indices:
    chunk = chunks[i]
    top_chunks.append(chunk)

  return top_chunks

client = InferenceClient("google/gemma-3-27b-it")

def respond(message,history):
    info = get_top_chunks(message)
    messages = [{"role": "system" , "content": f"Your name is BloomBot and you're a supportive and helpful chatbot catered towards teens ages 10-18. You give clear kid-appropiate explainations with {info} and keep your explainations to 10 sentences maximum." 
                }]
    if history:
        messages.extend(history)
        
    messages.append({"role" : "user", "content" : message})
    
    response = ""
        
    for message in client.chat_completion(
        messages, 
        max_tokens = 500,
        stream=True,
        #temperature = .2
        top_p = .2
    ):
        token = message.choices[0].delta.content
        response += token
       # print(response)
    yield response 
        

#theme = gr.themes.Ocean(
#    primary_hue="pink",
#    secondary_hue="pink",
#    neutral_hue="fuchsia"
#    )

def display_image():
    return "Screenshot 2025-06-12 at 10.53.59 AM.png"

with gr.Blocks (theme = theme) as chatbot:
    gr.Image(display_image())
    gr.ChatInterface(respond, type = "messages", #theme = gr.themes.Soft(
    #primary_hue="pink",
    #secondary_hue="pink",
    #neutral_hue="fuchsia"),
    title = "Hi, I'm BloomBot! 🌸",
    description = "This tool is here to listen and provide information on female health topics, and all discussions will be kept confidential. ❤️‍🩹",
    examples = ["What are the different types of period products? ", 
                "What are some vitamins that are good for teenage girls?",
                "What should I know about puberty?",
                "Where can I find my nearest OBGYN?"]
                          )
    with gr.Tab("Resources"):
        gr.Markdown("### Resources")
        gr.HTML("""
        <a href="https://drive.google.com/file/d/1_KNELAUDLLidwAT3fs2JBuO1yPgMGoDv/view" target="_blank">
         #   <button style="font-size:16px;padding:10px 20px;margin-top:10px;">
         #       📄 Period Tracker
         #   </button>
        </a>
   # """)
         
     #with gr.Tab("Educational PDFs"):
       # gr.Markdown("### 📘 Helpful Resources")

chatbot.launch(debug=True)