import streamlit as st
import streamlit.components.v1 as components
import requests
import os
import time
import streamlit as st
import base64
from streamlit_mic_recorder import mic_recorder
from notion_client import Client
import pandas as pd
MODEL_NAME = "drinktoomuchsax/whisper-small-hi"
lang = "en"

from threading import Thread
os.environ["COQUI_TOS_AGREED"] = "1"
os.environ["TRAINER_TELEMETRY"]= "0"
# Constants

HF_TOKEN = os.environ.get("HF_TOKEN", None)
BASETEN_API = os.environ.get("BASETEN_API", None)
BASETEN_KEY = os.environ.get("BASETEN_KEY", None)
NOTION_KEY = os.environ.get("NOTION_API_KEY", None)
NOTION_DB_ID = os.environ.get("NOTION_DB_ID", None)

notion = Client(auth=f"{NOTION_KEY}")

database_id = f"{NOTION_DB_ID}"

st.set_page_config(layout="wide")
# Load custom CSS to integrate Bootstrap, Font Awesome, and Google Fonts
st.markdown('''
    <link href="https://fonts.googleapis.com/css?family=Amatic+SC:400,700|Dosis:400,500,700&subset=latin,latin-ext" rel="stylesheet">
    <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.5.0/css/font-awesome.min.css" rel="stylesheet">
    <link href="https://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css" rel="stylesheet">
    <style>
        body {
            font-family: 'Dosis', sans-serif;
        }
        h1, h2, h3 {
            font-family: 'Amatic SC', cursive;
        }
        .calculator button {
            width: 100%;
            padding: 20px;
            font-size: 24px;
            margin: 5px;
        }
    </style>
''', unsafe_allow_html=True)

# Title Section
st.markdown('<h1 class="display-4 text-center">My Streamlit Application</h1><p class="lead text-center">Integrating Streamlit with Bootstrap Carousel</p>', unsafe_allow_html=True)



left, ml, right = st.columns([3,3,3])


with left:
    # Carousel Structure
    st.markdown('''<h3><i class="fa fa-calculator"></i> Calculator</h3>''', unsafe_allow_html=True)

    # Box 1: Calculator
    # Define the calculator layout
    buttons = [
        ['7', '8', '9', '/'],
        ['4', '5', '6', '\*'],
        ['1', '2', '3', '\-'],
        ['C', '0', '.', '\+'],
        ['=']
    ]

    # To store the calculation input
    if 'calc_input' not in st.session_state:
        st.session_state.calc_input = ""

    # Custom calculation function
    def calculate(expression):
        expression = expression.replace("\\","")
        try:
            result = eval(expression)  # placeholder for a safe eval replacement
            return str(result)
        except ZeroDivisionError:
            return "Error: Division by zero"
        except Exception:
            return "Error"

    # Display the calculator buttons
    for row in buttons:
        cols = st.columns(len(row))
        for i, btn_label in enumerate(row):
            if btn_label and cols[i].button(btn_label):
                if btn_label == '=':
                    st.session_state.calc_input = calculate(st.session_state.calc_input)
                elif btn_label == 'C':
                    st.session_state.calc_input = ""
                else:
                    st.session_state.calc_input += btn_label.replace("\\","")

    # Display the current calculation input/output
    st.text_input("Calculation", st.session_state.calc_input, key="display", disabled=True)

with ml:

    st.markdown('''<h3><i class="fa fa-image"></i> Gen Image</h3>''', unsafe_allow_html=True)

    api_key = f"{BASETEN_KEY}"
    negative_prompt = st.text_input("Negative Prompt", "blurry, text, low quality")
    positive_prompt = st.text_input("Positive Prompt", "An igloo on a snowy day, 4k, hd")
    controlnet_image_url = st.text_input("ControlNet Image URL", "https://storage.googleapis.com/logos-bucket-01/baseten_logo.png")



    # Button to trigger generation
    if st.button("Generate Prompt"):
       # Making the API request
        response = requests.post(
            "https://model-7wlx9oew.api.baseten.co/production/predict",
            headers={"Authorization": f"Api-Key {api_key}"},
            json={
                'workflow_values': {
                    'negative_prompt': negative_prompt,
                    'positive_prompt': positive_prompt,
                    'controlnet_image': controlnet_image_url
                }
            }
        )
        
        # Display the response
        if response.status_code == 200:
            result = response.json().get("result")
            if result:
                image_data = result[0].get("data")
                if image_data:
                    # Decode the base64 image data
                    image = base64.b64decode(image_data)
                    # Display the image in Streamlit
                    st.image(image, caption="Generated Image", use_column_width=True)
                else:
                    st.error("No image data found in the response.")
            else:
                st.error("No result found in the response.")
        else:
            st.error(f"Error: {response.status_code}, {response.text}")


# with rl:
#     # End of Box 2 and second Carousel Item
#     st.markdown('''<h3><i class="fa fa-pencil"></i> Transcribe </h3>''', unsafe_allow_html=True)

#     # Box 3: Form 2
#     # Audio recording using mic_recorder
#     audio = mic_recorder(
#         start_prompt="Start recording",
#         stop_prompt="Stop recording",
#         just_once=False,
#         use_container_width=False,
#         callback=None,
#         key="mic_recorder"
#     )

#     #uploaded_file = st.file_uploader("Or upload an audio file", type=["mp3", "wav", "flac", "aac"])
#     if st.button("Transcribe"):
#         if audio and "bytes" in audio:
#             st.success("Recording detected. Transcribing your recording...")
#             with open("temp_recording.wav", "wb") as f:
#                 f.write(audio["bytes"])
#             with st.spinner("Transcribing..."):
#                 #transcription = transcribe("temp_recording.wav")
#                 #need to send the data here
#                 transcription = "Under Process"
#                 print("")
#             st.text_area("Transcription", transcription, height=200)
#         else:
#             st.error("Please record audio or upload a file to transcribe.")    
with right:

    st.markdown('''<h3><i class="fa fa-pencil"></i> Chat with Mistral</h3>''', unsafe_allow_html=True)
    # Box 4: Form 3
    prompt3 = st.text_input("Enter Prompt", key="prompt3", value="Why is Sky Blue?")
    #image_url3 = st.text_input("Enter Image URL", key="image_url3")
    if st.button("Submit", key="submit3"):
        payload = {"prompt": prompt3}
        headers = {
            "Authorization": f"Api-Key {BASETEN_KEY}"
        }
        response = requests.post(f"{BASETEN_API}", headers=headers, json=payload)
        if response.status_code == 200:
            st.write(f"**Response:** {response.json()}")
        else:
            st.write("Failed to get a response")

    # End of Box 4 and fourth Carousel Item

left, middle, right = st.columns([1,3,1])

with middle:
    # Streamlit form for data input
    with st.form(key='data_entry_form'):
        name = st.text_input("Name")
        age = st.number_input("Age", min_value=0)
        location = st.text_input("Location")
        submit_button = st.form_submit_button(label='Submit')
    # Function to add data to Notion
    def add_to_notion(name, age, location):
        new_page = {
            "Name": {
                "title": [
                    {
                        "text": {
                            "content": name
                        }
                    }
                ]
            },
            "Age": {
                "number": age
            },
            "Location": {
                "rich_text": [
                    {
                        "text": {
                            "content": location
                        }
                    }
                ]
            }
        }    
        notion.pages.create(parent={"database_id": database_id}, properties=new_page)
    
    # Add data to Notion when form is submitted
    if submit_button:
        add_to_notion(name, age, location)
        st.success("Data submitted to Notion!")
    
    # Function to retrieve data from Notion
    def retrieve_data_from_notion():
        query_result = notion.databases.query(database_id=database_id)
        data = []
        for result in query_result["results"]:
            # Safely extract the "Name" property
            name = result["properties"]["Name"]["title"][0]["text"]["content"] if result["properties"]["Name"]["title"] else "No Name"
            
            # Safely extract the "Age" property
            age = result["properties"]["Age"]["number"] if result["properties"]["Age"]["number"] is not None else "No Age"
            
            # Safely extract the "Location" property
            location = result["properties"]["Location"]["rich_text"][0]["text"]["content"] if result["properties"]["Location"]["rich_text"] else "No Location"
            
            data.append({"Name": name, "Age": age, "Location": location})
        
        return pd.DataFrame(data)

    
    # Display the data in a table
    st.subheader("Stored Data")
    data_df = retrieve_data_from_notion()
    st.table(data_df)

hide_default_format = """
       <style>
       #MainMenu {visibility: hidden; }
       footer {visibility: hidden;}
       header {visibility: hidden;}
       </style>
       """
st.markdown(hide_default_format, unsafe_allow_html=True)