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| import gradio as gr | |
| import requests | |
| import os | |
| ##Bloom | |
| API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom" | |
| HF_TOKEN = os.environ["HF_TOKEN"] | |
| headers = {"Authorization": f"Bearer {HF_TOKEN}"} | |
| def sql_generate(prompt, input_prompt_sql ): | |
| print(f"*****Inside SQL_generate - Prompt is :{prompt}") | |
| if input_prompt_sql != '': | |
| prompt = "Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: " +input_prompt_sql + "\nPostgreSQL query: " | |
| json_ = {"inputs": prompt, | |
| "parameters": | |
| { | |
| "top_p": 0.9, | |
| "temperature": 1.1, | |
| "max_new_tokens": 250, | |
| "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}") | |
| output_tmp = output[0]['generated_text'] | |
| print(f"output_tmp is: {output_tmp}") | |
| solution = output_tmp.split("\nQ:")[0] #output[0]['generated_text'].split("Q:")[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\nThis Space is created by [Yuvraj Sharma](https://twitter.com/yvrjsharma) for EuroPython 2022 Demo.\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*, don't despair and try again after few minutes. Still iterating over the app, might add new features soon.""" | |
| ) | |
| 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") | |
| input_prompt_sql = gr.Textbox(label="Or Write text following the above pattern to get SQL commands...", value="Instruction: Given an input question, respond with syntactically correct PostgreSQL. Only use table called 'department'.\nInput: Select names of all the departments in descending alphabetical order of department names.\nPostgreSQL query: ", lines=5) | |
| 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) | |
| demo.launch(enable_queue=True, debug=True) |