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| import gradio as gr | |
| import subprocess | |
| import pandas as pd | |
| ''' | |
| import gradio as gr | |
| def greet(name): | |
| return "Hello " + name + "!" | |
| demo = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| demo.launch() | |
| ''' | |
| def greet(name1, name2): | |
| # Storing each input in a variable, you can process or save them as you like | |
| str1_openai = name1 ## openai | |
| str2_bioportal = "213e22ba-4c3b-402b-bd36-6e9d4e86b1b5" #bioportal | |
| str3_huggingface = "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" # huggingface | |
| str4_input = name2 | |
| with open('abstractsave.txt', 'w') as f: | |
| f.write(str4_input) | |
| def run_command(command): | |
| result = subprocess.run(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) | |
| output_string = result.stdout | |
| error_string = result.stderr | |
| return output_string, error_string | |
| ##### output_string1, error_string1= run_command("pip install optogpt") | |
| output_string1, error_string1 = run_command("curl -sSL https://install.python-poetry.org | python3 -") | |
| output_string2, error_string2 = run_command(f"poetry run runoak set-apikey -e openai {str1_openai}") | |
| run_command(f"poetry run runoak set-apikey -e bioportal {str2_bioportal}") | |
| run_command(f"poetry run runoak set-apikey -e hfhub-key {str3_huggingface}") | |
| ##### output = run_command(f"ontogpt extract -t gocam.GoCamAnnotations -i ./abstract.txt") | |
| output = run_command(f"cancerontogpt extract -t cancer.CancerAnnotations -i ./abstractsave.txt") | |
| output = output[0].replace('\\n', '\n') | |
| # Find the positions of the start and end markers | |
| start_marker = "raw_completion_output: |-" | |
| end_marker = "prompt: " | |
| start_position = output.find(start_marker) | |
| end_position = output.find(end_marker) | |
| # Extract the text between the start and end positions | |
| output = output[start_position + len(start_marker):end_position].strip() | |
| # Splitting the data string into lines and then split each line into key-value pairs | |
| key_value_pairs = [line.split(": ", 1) for line in output.split("\n")[1:] if line.strip()] | |
| def format_identifier(identifier: str) -> str: | |
| # Split the string by underscores | |
| words = identifier.strip().split('_') | |
| # Capitalize each word | |
| capitalized_words = [word.capitalize() for word in words] | |
| # Join the words with spaces | |
| formatted_identifier = ' '.join(capitalized_words) | |
| return formatted_identifier | |
| key_value_pairs = [[format_identifier(x[0]), *x[1:]] for x in key_value_pairs] | |
| # Convert the key-value pairs into a table format (a list of lists) | |
| df_pred = pd.DataFrame(key_value_pairs, columns = ["Ontology Attribute", "Value"]).iloc[:19,:] | |
| # data = { | |
| # "Name": ['fawef', 'fseaf', 'asef'], | |
| # "Age": [30, 25, 35], | |
| # "City": ["New York", "San Francisco", "Los Angeles"] | |
| # } | |
| # df = pd.DataFrame(data) | |
| return df_pred.to_html() | |
| #### output_string1, error_string1=run_command("poetry")# ontogpt") | |
| # return location | |
| # For the purpose of this example, I'm just returning the values concatenated | |
| # return f"Inputs received: {str1} \n, {str2}, {str3}, {str4}, '--------------', '--------------', {output_string1},{error_string1},{output_string2},{error_string2},{output}" | |
| # # return location | |
| # For the purpose of this example, I'm just returning the values concatenated | |
| # return f"{str4_input}" | |
| # Define 5 text input boxes with labels | |
| input_boxes = [ | |
| gr.inputs.Textbox(label="openai api key"), | |
| gr.inputs.Textbox(lines=20, label="Input cencer report", placeholder='Type text here...'), | |
| ] | |
| # iface = gr.Interface(fn=greet, inputs=input_boxes, outputs="text") | |
| iface = gr.Interface(fn=greet, inputs=input_boxes, outputs=gr.outputs.HTML(label="Output Table"),examples=[ # Sample text examples | |
| ["this is a sample text"], | |
| ["gradio is great"] | |
| ]) | |
| iface.launch() | |