app.py

# import gradio as gr
# import requests
# import os 

# ##Bloom
# API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"

# HF_TOKEN = "Bloom_Token"
# headers = {"Authorization": f"Bearer {HF_TOKEN}"}


# def sql_generate(prompt, input_prompt_sql ): 

#   print(f"*****Inside SQL_generate - Prompt is :{prompt}")
#   print(f"length of input_prompt_sql is {len(input_prompt_sql)}")
#   print(f"length of prompt is {len(prompt)}")
#   if len(prompt) == 0:
#     prompt = input_prompt_sql 
    
#   json_ = {"inputs": prompt,
#             "parameters":
#             {
#             "top_p": 0.9,
#           "temperature": 1.1,
#           "max_new_tokens": 64,
#           "return_full_text": False,
#           }, 
#           "options": 
#           {"use_cache": True,
#           "wait_for_model": True,
#           },}
#   response = requests.post(API_URL, headers=headers, json=json_)
#   print(f"Response  is : {response}")
#   output = response.json()
#   print(f"output is : {output}") 
#   output_tmp = output[0]['generated_text']
#   print(f"output_tmp is: {output_tmp}")
#   solution = output_tmp.split("\nQ:")[0]   
#   print(f"Final response after splits is: {solution}")
#   if '\nOutput:' in solution:
#     final_solution = solution.split("\nOutput:")[0] 
#     print(f"Response after removing output is: {final_solution}")
#   elif '\n\n' in solution:
#     final_solution = solution.split("\n\n")[0] 
#     print(f"Response after removing new line entries is: {final_solution}")
#   else:
#     final_solution = solution
#   return final_solution 


# demo = gr.Blocks()

# with demo:
#   gr.Markdown("<h1><center>Zero Shot SQL by Bloom</center></h1>")
#   gr.Markdown(
#         """[BigScienceW Bloom](https://twitter.com/BigscienceW) \n\n Large language models have demonstrated a capability of Zero-Shot SQL generation. Some might say — You can get good results out of LLMs if you know how to speak to them. This space is an attempt at inspecting this behavior/capability in the new HuggingFace BigScienceW [Bloom](https://huggingface.co/bigscience/bloom) model.\n\nThe Prompt length is limited at the API end right now, thus there is a certain limitation in testing Bloom's capability thoroughly.This Space might sometime fail due to inference queue being full and logs would end up showing error as *'queue full, try again later'*, in such cases please try again after few minutes. Please note that, longer prompts might not work as well and the Space could error out with Response code [500] or *'A very long prompt, temporarily not accepting these'* message in the logs. Still iterating over the app, might be able to improve it further soon.. \n\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for Gradio EuroPython 2022 Demo."""
#         )
#   with gr.Row():
  
#     example_prompt = gr.Radio( [
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: How many users signed up in the past month?\nPostgreSQL query: ", 
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: Create a query that displays empfname, emplname, deptid, deptname, location from employee table. Results should be in the ascending order based on the empfname and location.\nPostgreSQL query: ", 
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL. Only use table called 'employees'.\nInput: What is the total salary paid to all the employees?\nPostgreSQL query: ",
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL. Only use table called 'employees'.\nInput: List names of all the employees whose name end with 'r'.\nPostgreSQL query: ",
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL. Only use table called 'employees'.\nInput: What are the number of employees in each department?\nPostgreSQL query: ",
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL. Only use table called 'employees'.\nInput: Select names of all theemployees who have third character in their name as 't'.\nPostgreSQL query: ",
#     "Instruction: Given an input question, respond with syntactically correct PostgreSQL. Only use table called 'employees'.\nInput: Select names of all the employees who are working under 'Peter'.\nPostgreSQL query: ",  ], label= "Choose a sample Prompt")
  
#     #with gr.Column:  
#     input_prompt_sql = gr.Textbox(label="Or Write text following the example pattern given below, to get SQL commands...", value="Instruction: Given an input question, respond with syntactically correct PostgreSQL. Use table called 'department'.\nInput: Select names of all the departments in their descending alphabetical order.\nPostgreSQL query: ", lines=6)
    
#   with gr.Row():
#     generated_txt = gr.Textbox(lines=3)

#   b1 = gr.Button("Generate SQL")
#   b1.click(sql_generate,inputs=[example_prompt, input_prompt_sql], outputs=generated_txt) 

#   with gr.Row(): 
#     gr.Markdown("![visitor badge](https://visitor-badge.glitch.me/badge?page_id=europython2022_zero-shot-sql-by-bloom)")
    
# demo.launch(enable_queue=True, debug=True) 

import gradio as gr

gr.Interface.load("models/bigscience/bloom").launch()