import gradio as gr
import openai, os
from huggingface_hub import Repository
from io import BytesIO
from dotenv import load_dotenv
from openai.embeddings_utils import get_embedding, cosine_similarity
from ml_to_nl_translation.translation import getTranslations, getJSONDF
from lookups.translate_pdf_to_text import PreparePDF
from lookups.create_searchable_content import CreateSearchableContent
from utilities import api_keys
import PyPDF2

import pkg_resources
pypdf_version = pkg_resources.get_distribution("PyPDF2").version
print(f"python-pypdf_version version: {pypdf_version}")

openai.api_key = api_keys.APIKeys().get_key('OPENAI_API_KEY')
eleven_api_key = api_keys.APIKeys().get_key('ELEVEN_LABS_API_KEY')
voice_id = api_keys.APIKeys().get_key('VOICE_ID')

def fetch_translation():
     result=getTranslations()
     print("translator_wrapper")
     print (result)
     return result

def fetch_json_html():
     #reminder - this should reference a method that returns html, keeping as example
     result = getJSONDF()
     print("result")
     print(result)
     return f"<pre>{result}</pre>" 

def fetch_json_df():
     result = getJSONDF()
     print(result)
     return result

def fetch_reference():
     result = PreparePDF()
     print("Result"+result)
     return result

def fetch_content():
     result = CreateSearchableContent()
     return result

with gr.Blocks() as ui1:
    with gr.Row():
        b1 = gr.Button("Get Sensor Data")
    with gr.Row():
        with gr.Column(scale=1, min_width=600):
            df1 =gr.Dataframe(type="pandas")
    b1.click(fetch_json_df,outputs=df1)

with gr.Blocks() as ui2:
    with gr.Row():
        b2 = gr.Button("NLP Translate")
    with gr.Row():
        with gr.Column(scale=1, min_width=600):
            df2 =gr.Dataframe(type="pandas")
    b2.click(fetch_translation,outputs=df2)

with gr.Blocks() as ui3:
    with gr.Row():
        b3 = gr.Button("Pull Reference")
    with gr.Row():
        with gr.Column(scale=1, min_width=600):
            df3 =gr.HTML()
    b3.click(fetch_reference,outputs=df3)

with gr.Blocks() as ui4:
    with gr.Row():
        b4 = gr.Button("Create Searchable Content")
    with gr.Row():
        with gr.Column(scale=1, min_width=600):
            df4 =gr.Dataframe(type="pandas")
    b4.click(fetch_content,outputs=df4)

demo = gr.TabbedInterface([ui1,ui2,ui3,ui4], ("Sensor Data", "NLP Translation", "Pull Reference","Create Embeddings"))
demo.launch()