import streamlit as st
from llama_cpp import Llama

# llm = Llama.from_pretrained(
#     repo_id="Mykes/med_gemma7b_gguf",
#     filename="*Q4_K_M.gguf",
#     verbose=False,
#     n_ctx=512,
#     n_batch=512,
#     n_threads=4
# )
@st.cache_resource
def load_model():
    return Llama.from_pretrained(
        # repo_id="Mykes/med_gemma7b_gguf",
        # filename="*Q4_K_M.gguf",
        repo_id="Mykes/med_phi3-mini-4k-GGUF",
        filename="*Q4_K_M.gguf",
        verbose=False,
        n_ctx=256,
        n_batch=256,
        n_threads=4
    )

llm = load_model()

# basic_prompt = "Below is the context which is your conversation history and the last user question. Write a response according the context and question. ### Context: user: Ответь мне на вопрос о моем здоровье. assistant: Конечно! Какой у Вас вопрос? ### Question: {question} ### Response:"
basic_prompt = "Q: {question}\nA:"
input_text = st.text_input('text')
model_input = basic_prompt.format(question=input_text)

if input_text:
    # Create an empty placeholder for the output
    output_placeholder = st.empty()
    
    # Initialize an empty string to store the generated text
    generated_text = ""
    
    # Stream the output
    for token in llm(
        model_input,
        # max_tokens=32,
        max_tokens=None,
        stop=["<end_of_turn>"],
        echo=True,
        stream=True  # Enable streaming
    ):
        # Append the new token to the generated text
        generated_text += token['choices'][0]['text']
        
        # Update the placeholder with the current generated text
        output_placeholder.write(generated_text)

    # After the generation is complete, you can do any final processing if needed
    st.write("Generation complete!")