app.py

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"),
]

# 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="Patient John Smith (HSI-5421) underwent a tissue biopsy at the bladder collection site. The histopathology report confirmed the presence of cancer in the bladder. The tumor, identified as T-BC5421, measures 4 cm in its longest dimension. The cancer has been staged as T2, indicating its progression beyond the bladder wall. The cancer was asserted on January 10, 2022. The tumor marker test revealed elevated levels of urinary bladder tumor antigen (UBTA). John Smith, a Caucasian male born on April 15, 1975, is non-Hispanic and resides in zip code 90210. He is still alive, with no reported death date at this time. For any further information, please contact John Smith at [email protected] or (555) 123-4567."
                                    )

iface.launch()