import os
import openai
import PyPDF2
import gradio as gr
import docx
import re


class Resume_Overall:
    def __init__(self):
        pass

    def extract_text_from_file(self,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()

        elif file_extension == '.docx':
              doc = docx.Document(file_path)
              text = []
              for paragraph in doc.paragraphs:
                  text.append(paragraph.text)
              return '\n'.join(text)

        else:
            return "Unsupported file type"

    def course_response(self,resume_path):
        resume_path = resume_path.name
        resume = self.extract_text_from_file(resume_path)


        # Define the prompt or input for the model
        prompt = f"""Analyze the resume to generate online courses with website links to improve skills following resume delimitted by triple backticks. Generate atmost five courses.
                     result format should be:
                     course:[course].
                     website link:[website link]
                     ```{resume}```
                  """

        # Generate a response from the GPT-3 model
        response = openai.Completion.create(
            engine='text-davinci-003',
            prompt=prompt,
            max_tokens=200,
            temperature=0,
            n=1,
            stop=None,
        )

        # Extract the generated text from the API response
        generated_text = response.choices[0].text.strip()

        return generated_text
    def summary_response(self,resume_path):
        resume_path = resume_path.name
        resume = self.extract_text_from_file(resume_path)


        # Define the prompt or input for the model
        prompt = f"""Analyze the resume to write the summary for following resume delimitted by triple backticks.
                     ```{resume}```
                  """

        # Generate a response from the GPT-3 model
        response = openai.Completion.create(
            engine='text-davinci-003',
            prompt=prompt,
            max_tokens=200,
            temperature=0,
            n=1,
            stop=None,
        )

        # Extract the generated text from the API response
        generated_text = response.choices[0].text.strip()

        return generated_text
  

    def skill_response(self,job_description_path):
        job_description_path = job_description_path.name
        resume = self.extract_text_from_file(job_description_path)


        # Define the prompt or input for the model
        prompt = f"""Find Education Gaps in given resume. Find Skills in resume.
                      ```{resume}```
                  """

        # Generate a response from the GPT-3 model
        response = openai.Completion.create(
            engine='text-davinci-003',  # Choose the GPT-3 engine you want to use
            prompt=prompt,
            max_tokens=100,  # Set the maximum number of tokens in the generated response
            temperature=0,  # Controls the randomness of the output. Higher values = more random, lower values = more focused
            n=1,  # Generate a single response
            stop=None,  # Specify an optional stop sequence to limit the length of the response
        )

        # Extract the generated text from the API response
        generated_text = response.choices[0].text.strip()

        return generated_text

    def _generate_job_list(self, resume: str) -> str:
        prompt = f"List out perfect job roles for based on resume informations:{resume}"
        response = openai.Completion.create(
            engine='text-davinci-003',
            prompt=prompt,
            max_tokens=100,
            temperature=0,
            n=1,
            stop=None,
        )
        generated_text = response.choices[0].text.strip()
        return generated_text


    def job_list_interface(self, file) -> str:
        resume_text = self.extract_text_from_file(file.name)

        job_list = self._generate_job_list(resume_text)
        return job_list

    def generate_job_description(self, role, experience):
        # Generate a response from the GPT-3 model

        prompt = f"""Your task is generate Job description for this {role} with {experience} years of experience.
                    Job Description Must have
                    1. Job Title
                    2. Job Summary : [200 words]
                    3. Responsibilities : Five Responsibilities in five lines
                    4. Required Skills : Six Skills
                    5. Qualifications
                  These topics must have in that Generated Job Description.
                  """
        response = openai.Completion.create(
            engine='text-davinci-003',  # Choose the GPT-3 engine you want to use
            prompt=prompt,
            max_tokens=500,  # Set the maximum number of tokens in the generated response
            temperature=0.5,  # Controls the randomness of the output. Higher values = more random, lower values = more focused
        )

        # Extract the generated text from the API response
        generated_text = response.choices[0].text.strip()

        return generated_text  
        
    def response(self,job_description_path):
        job_description_path = job_description_path.name
        job_description = self.extract_text_from_file(job_description_path)


        # Define the prompt or input for the model
        prompt = f"""Generate interview questions for screening following job_description delimitted by triple backticks. Generate atmost ten questions.
                     ```{job_description}```
                  """

        # Generate a response from the GPT-3 model
        response = openai.Completion.create(
            engine='text-davinci-003',  # Choose the GPT-3 engine you want to use
            prompt=prompt,
            max_tokens=200,  # Set the maximum number of tokens in the generated response
            temperature=0,  # Controls the randomness of the output. Higher values = more random, lower values = more focused
            n=1,  # Generate a single response
            stop=None,  # Specify an optional stop sequence to limit the length of the response
        )

        # Extract the generated text from the API response
        generated_text = response.choices[0].text.strip()

        return generated_text 
        
    def show_file(self,file_path):
      return file_path.name

    def launch_gradio_interface(self, share: bool = True):
        with gr.Blocks(css="style.css",theme='freddyaboulton/test-blue') as app:
            with gr.Tab("Resume"):
                with gr.Row(elem_id="col-container"):
                    with gr.Column(scale=0.70):
                        file_output = gr.File(elem_classes="filenameshow")
                    with gr.Column(scale=0.30):    
                        upload_button = gr.UploadButton(
                            "Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
                            elem_classes="uploadbutton",scale=0.60)
  
                with gr.TabItem("Designation"):
                    with gr.Column(elem_id = "col-container",scale=0.80):
                        btn = gr.Button(value="Submit")
                        output_text = gr.Textbox(label="Designation List",lines=8)
                with gr.TabItem("Summarized"):  
                    with gr.Column(elem_id = "col-container",scale=0.80):
                        analyse = gr.Button("Analyze")
                        summary_result = gr.Textbox(label="Summarized",lines=8)      
                with gr.TabItem("Skills and Education Gaps"):  
                    with gr.Column(elem_id = "col-container",scale=0.80):
                        analyse_resume = gr.Button("Analyze Resume")
                        result = gr.Textbox(label="Skills and Education Gaps",lines=8)
                with gr.TabItem("Course"): 
                    with gr.Column(elem_id = "col-container",scale=0.80):
                        course_analyse = gr.Button("Find Courses")
                        course_result = gr.Textbox(label="Suggested Cources",lines=8)
        
                upload_button.upload(self.show_file,upload_button,file_output)
                course_analyse.click(self.course_response, [upload_button], course_result)
                analyse_resume.click(self.skill_response, [upload_button], result)
                btn.click(self.job_list_interface, upload_button, output_text)
                analyse.click(self.summary_response, [upload_button], summary_result)
                
            with gr.Tab("Job Description"):
                with gr.Row(elem_id="col-container"):
                    with gr.Column(scale=0.70):
                        file_output1 = gr.File(elem_classes="filenameshow")
                    with gr.Column(scale=0.30):    
                        upload_button1 = gr.UploadButton(
                            "Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
                            elem_classes="uploadbutton")  
                with gr.TabItem("Screening Question"):        
                    with gr.Row(elem_id="col-container"):        
                            jd_btn = gr.Button(value="Submit")
                    with gr.Row(elem_id="col-container"):      
                            output_text1 = gr.Textbox(label="Screening Question") 
                with gr.TabItem("Generate JD"):    
                    with gr.Row(elem_id="col-container"):
                        with gr.Column(scale=0.50):
                             rolls = gr.Textbox(label="Role",scale=0.60)
                        with gr.Column(scale=0.50):    
                             experience = gr.Textbox(label="Experience",scale=0.60)
                    with gr.Row(elem_id="col-container"):
                        get_jd_btn = gr.Button("Generate JD")
                    with gr.Row(elem_id="col-container"):
                        result_jd = gr.Textbox(label="Job Description",lines=8)
                            
                get_jd_btn.click(self.generate_job_description, [rolls,experience], result_jd)      
                upload_button1.upload(self.show_file,upload_button1,file_output1)        
                jd_btn.click(self.response,[upload_button1], output_text1)
                
        app.launch(debug=True)

if __name__ == "__main__":
    resume_overall = Resume_Overall()
    resume_overall.launch_gradio_interface()