import streamlit as st
from huggingface_hub import InferenceClient
import fitz  # PyMuPDF
import os
import tempfile

st.title("ChatGPT-like Chatbot")

base_url = "https://api-inference.huggingface.co/models/"
API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
headers = {"Authorization": "Bearer " + str(API_KEY)}

model_links = {
    "Mistral-7B": base_url + "mistralai/Mistral-7B-Instruct-v0.2"
}

model_info = {
    "Mistral-7B": {
        #'description': "Good Model",
        #'logo': 'model.jpg'
    }
}

def format_prompt(context, question, custom_instructions=None):
    prompt = ""
    if custom_instructions:
        prompt += f"[INST] {custom_instructions} [/INST]"
    prompt += f"{context}\n\n[INST] {question} [/INST]"
    return prompt

def reset_conversation():
    st.session_state.conversation = []
    st.session_state.messages = []
    return None

def read_pdf(file):
    with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
        tmp_file.write(file.read())
        tmp_file_path = tmp_file.name
    
    pdf_document = fitz.open(tmp_file_path)
    text = ""
    for page_num in range(len(pdf_document)):
        page = pdf_document[page_num]
        text += page.get_text()
    
    os.remove(tmp_file_path)
    return text

models = [key for key in model_links.keys()]

# Create the sidebar with the dropdown for model selection
selected_model = st.sidebar.selectbox("Select Model", models)

# Create a temperature slider
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))

# Add reset button to clear conversation
st.sidebar.button('Reset Chat', on_click=reset_conversation)  # Reset button

# Create model description
st.sidebar.write(f"You're now chatting with {selected_model}")
#st.sidebar.markdown(model_info[selected_model]['description'])
#st.sidebar.image(model_info[selected_model]['logo'])
st.sidebar.markdown("Generated content may be inaccurate or false.")
st.sidebar.markdown("\nLearn how to build this chatbot here.")

if "prev_option" not in st.session_state:
    st.session_state.prev_option = selected_model

if st.session_state.prev_option != selected_model:
    st.session_state.messages = []
    st.session_state.prev_option = selected_model
    reset_conversation()

# Pull in the model we want to use
repo_id = model_links[selected_model]

st.subheader(f'AI - {selected_model}')
st.title(f'ChatBot Using {selected_model}')

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Upload PDF

with st.sidebar:
    uploaded_file = st.file_uploader("Choose a PDF file", type="pdf")
    if uploaded_file is not None:
        pdf_text = read_pdf(uploaded_file)
        st.session_state.pdf_text = pdf_text
        st.write("PDF content loaded successfully!")

# Accept user input
if prompt := st.chat_input(f"Hi I'm {selected_model}, ask me a question"):

    custom_instruction = "Act like a Human in conversation"

    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

    context = st.session_state.pdf_text if "pdf_text" in st.session_state else ""
    formated_text = format_prompt(context, prompt, custom_instruction)

    # Display assistant response in chat message container
    with st.chat_message("assistant"):
        client = InferenceClient(
            model=model_links[selected_model],
            headers=headers)

        output = client.text_generation(
            formated_text,
            temperature=temp_values,  # 0.5
            max_new_tokens=3000,
            stream=True
        )

        response = st.write_stream(output)
    st.session_state.messages.append({"role": "assistant", "content": response})