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test initial
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app.py
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import gradio as gr
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import requests
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import os
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##Bloom
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API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
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HF_TOKEN = "Bloom_Token"
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headers = {"Authorization": f"Bearer {HF_TOKEN}"}
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def sql_generate(prompt, input_prompt_sql ):
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demo = gr.Blocks()
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with demo:
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demo.launch(enable_queue=True, debug=True)
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# import gradio as gr
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# import requests
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# import os
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# ##Bloom
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# API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
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# HF_TOKEN = "Bloom_Token"
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# headers = {"Authorization": f"Bearer {HF_TOKEN}"}
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# def sql_generate(prompt, input_prompt_sql ):
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# print(f"*****Inside SQL_generate - Prompt is :{prompt}")
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# print(f"length of input_prompt_sql is {len(input_prompt_sql)}")
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# print(f"length of prompt is {len(prompt)}")
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# if len(prompt) == 0:
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# prompt = input_prompt_sql
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# json_ = {"inputs": prompt,
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# "parameters":
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# {
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# "top_p": 0.9,
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# "temperature": 1.1,
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# "max_new_tokens": 64,
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# "return_full_text": False,
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# },
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# "options":
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# {"use_cache": True,
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# "wait_for_model": True,
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# },}
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# response = requests.post(API_URL, headers=headers, json=json_)
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# print(f"Response is : {response}")
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# output = response.json()
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# print(f"output is : {output}")
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# output_tmp = output[0]['generated_text']
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# print(f"output_tmp is: {output_tmp}")
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# solution = output_tmp.split("\nQ:")[0]
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# print(f"Final response after splits is: {solution}")
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# if '\nOutput:' in solution:
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# final_solution = solution.split("\nOutput:")[0]
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# print(f"Response after removing output is: {final_solution}")
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# elif '\n\n' in solution:
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# final_solution = solution.split("\n\n")[0]
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# print(f"Response after removing new line entries is: {final_solution}")
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# else:
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# final_solution = solution
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# return final_solution
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# demo = gr.Blocks()
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# with demo:
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# gr.Markdown("<h1><center>Zero Shot SQL by Bloom</center></h1>")
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# gr.Markdown(
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# """[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."""
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# )
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# with gr.Row():
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# example_prompt = gr.Radio( [
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# "Instruction: Given an input question, respond with syntactically correct PostgreSQL\nInput: How many users signed up in the past month?\nPostgreSQL query: ",
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# "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: ",
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# "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: ",
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# "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: ",
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# "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: ",
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# "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: ",
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# "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")
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# #with gr.Column:
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# 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)
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# with gr.Row():
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# generated_txt = gr.Textbox(lines=3)
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# b1 = gr.Button("Generate SQL")
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# b1.click(sql_generate,inputs=[example_prompt, input_prompt_sql], outputs=generated_txt)
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# with gr.Row():
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# gr.Markdown("")
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# demo.launch(enable_queue=True, debug=True)
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import gradio as gr
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gr.Interface.load("models/bigscience/bloom").launch()
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