import streamlit as st
import re
import pandas as pd
import numpy as np
import time
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Streamlit app
st.title("Private Sample")
tokenizer = T5Tokenizer.from_pretrained('t5-small')

# Load the model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = T5ForConditionalGeneration.from_pretrained('cssupport/t5-small-awesome-text-to-sql')
model = model.to(device)
model.eval()

def generate_sql(input_prompt):
    # Tokenize the input prompt
    inputs = tokenizer(input_prompt, padding=True, truncation=True, return_tensors="pt").to(device)
    
    # Forward pass
    with torch.no_grad():
        outputs = model.generate(**inputs, max_length=512)
    
    # Decode the output IDs to a string (SQL query in this case)
    generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return generated_sql
prompt=st.text_input("Enter Prompt: ","get target from app saless")
button_clicked=st.button("Generate")
if button_clicked:
    input_prompt = "tables:\n" + "CREATE TABLE AppDrug_allergy_dataset( Id,ExtraProperties,ConcurrencyStamp,CreationTime,CreatorId,LastModificationTime,LastModifierId,IsDeleted,DeleterId,DeletionTime,Drug_Name,Chemical_Structure,Immunogenecity,Individual_Sensitivity,Prior_Allergic_Reaction,Cross_Reactivity,Route_of_administration,Dose,Duration,Hypersensitivity_Reaction,Allergic) CREATE TABLE AppSaless( Id,ExtraProperties,ConcurrencyStamp,CreationTime, CreatorId,LastModificationTime,LastModifierId,IsDeleted,DeleterId,DeletionTime,Month,Target,Customers_,Revenue)" + "\n" +"query for:" + prompt
    generated_sql = generate_sql(input_prompt)
    print(f"The generated SQL query is: {generated_sql}")
# Test the function
#input_prompt = "tables:\n" + "CREATE TABLE Catalogs (date_of_latest_revision VARCHAR)" + "\n" +"query for: Find the dates on which more than one revisions were made."
#input_prompt = "tables:\n" + "CREATE TABLE table_22767 ( \"Year\" real, \"World\" real, \"Asia\" text, \"Africa\" text, \"Europe\" text, \"Latin America/Caribbean\" text, \"Northern America\" text, \"Oceania\" text )" + "\n" +"query for:what will the population of Asia be when Latin America/Caribbean is 783 (7.5%)?."
# input_prompt =  "Retrieve the names of all employees who work in the IT department."


#OUTPUT: The generated SQL query is: SELECT student_id FROM students WHERE NOT student_id IN (SELECT student_id FROM student_course_attendance)

progress_bar = st.progress(0)
status_text = st.empty()
chart = st.line_chart(np.random.randn(10, 2))

for i in range(100):
    # Update progress bar.
    progress_bar.progress(i + 1)

    new_rows = np.random.randn(10, 2)

    # Update status text.
    status_text.text(
        'The latest random number is: %s' % new_rows[-1, 1])

    # Append data to the chart.
    chart.add_rows(new_rows)

    # Pretend we're doing some computation that takes time.
    time.sleep(0.1)

status_text.text('Done!')
st.balloons()