import gradio as gr
import PyPDF2
import os
import openai

def extract_text_from_file(file_path):
    # Get the file extension
    file_extension = os.path.splitext(file_path)[1]

    if file_extension == '.pdf':
        with open(file_path, 'rb') as file:
            # Create a PDF file reader object
            reader = PyPDF2.PdfFileReader(file)

            # Create an empty string to hold the extracted text
            extracted_text = ""

            # Loop through each page in the PDF and extract the text
            for page_number in range(reader.getNumPages()):
                page = reader.getPage(page_number)
                extracted_text += page.extractText()
        return extracted_text

    elif file_extension == '.txt':
        with open(file_path, 'r') as file:
            # Just read the entire contents of the text file
            return file.read()

    else:
        return "Unsupported file type"

def responce_from_ai(textjd, textcv):
    resume = extract_text_from_file(textjd)
    job_description = extract_text_from_file(textcv)
    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=f"""
                    Given the job description and the resume, assess the matching percentage and approximate percentage of the resume for the job.**Job Description:**{job_description}**Resume:**{resume}**Matching Assessment:**Based on an analysis of the resume and the job description,
the overall matching percentage is estimated to be approximately [insert approximate percentage here].
**Detailed Analysis:**
                the result should be in this format:
                matched percentage: [matching percentage]
                reason            : [reason for this result]
                keywords          : [matched key words from job_description and resume]
                """,
        temperature=0,
        max_tokens=100,
        n=1,
        stop=None,
    )
    generated_text = response.choices[0].text.strip()
    return generated_text

def matching_percentage(job_description_path, resume_path):

    job_description_path = job_description_path.name
    resume_path = resume_path.name

    generated_text = responce_from_ai(job_description_path, resume_path)
    return generated_text


with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as app:
      gr.HTML("""<img class="leftimage" align="left" src="https://templates.images.credential.net/1612472097627370951721412474196.png" alt="Image" width="210" height="210">
                 <img class="leftimage" align="left" src="https://companieslogo.com/img/orig/RAND.AS_BIG-0f1935a4.png?t=1651813778" alt="Image" width="210" height="210">""")

      with gr.Row():
        with gr.Column(elem_id="col-container"):
          gr.HTML(
              """<br style="color:white;">"""
              )
          gr.HTML(
              """<h2 style="text-align:center; color:"white">Randstad Resume Matcher</h2> """
          )
          gr.HTML("<br>")
      with gr.Row():
        with gr.Column(scale=0.45, min_width=150, ):
          jobDescription = gr.inputs.File(label="Job Description")
        with gr.Column(scale=0.45, min_width=150):
          resume = gr.inputs.File(label="Resume")
        with gr.Column(scale=0.10, min_width=150):
          find = gr.Button("Find")
      with gr.Row():
        with gr.Column(scale=1.0, min_width=150):
          output = gr.outputs.Textbox(label="Matching Percentage")

      find.click(matching_percentage, [jobDescription, resume], [output])
app.launch()