import gradio as gr
import os
import fitz
from openai import AzureOpenAI
import re

# client = AzureOpenAI(api_key=os.getenv("AZURE_OPENAI_KEY"),  
#                     api_version="2023-07-01-preview",
#                     azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
#                      )

client = AzureOpenAI()

class ResumeAnalyser:
    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':
            # Use PyMuPDF (fitz) for PDF text extraction
            doc = fitz.open(file_path)
            extracted_text = ""
            for page in doc:
                extracted_text += page.get_text()
            doc.close()
            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(self,job_description_path, resume_list_path):
    
      result = ""
      job_description = self.extract_text_from_file(job_description_path.name)
      for resume_path in resume_list_path:
        resume = self.extract_text_from_file(resume_path.name)
        # Create a conversation for the OpenAI chat API
        conversation = [
            {"role": "system", "content": "You are a Mental Healthcare Chatbot."},
            {"role": "user", "content": f"""Given the job description and the resume, assess the matching percentage to 100 and if 100 percentage not matched mention the remaining percentage with reason. **Job Description:**{job_description}**Resume:**{resume}
          **Detailed Analysis:**
          Introduction to say we've assessment the resume
                  the result should be in this format:
                  Matched Percentage: Precisely [get matching percentage between job description and resume]%.\n
                  Qualification Matching Percentage: [matching percentage between job description and resume qualifications].\n
                  Skills Matching Percentage: [matching percentage between job description and resume skills].\n
                  Experience Matching Percentage: [matching percentage between job description and resume experience].\n
                  Reason            : [Reasons for why this resume matched and not matched.].\n
                  Skills To Improve : [Mention the skills to improve for the candidate according to the given job description. If there are no matches, simply say N/A.].\n
                  Keywords          : [Return the matched keywords from resume and job_description. If there are no matches, simply say N/A.]\n
                  Company : [Extracted company name from job description].\n
                  Irrevelant: [mention the irrevelant skills and expericence]\n
                  Recommend Course:  [mention specific course to recommend the candidate for job description needs].\n
                  Experience: [mention specific experience to recommend the candidate for job description needs].\n
                  Tailor Your Application: [Emphasize relevant areas].\n
                  Certifications: [Pursue certifications in mention area].\n
                  Feel free to contact us for further clarification.\n
                  Best wishes,
          Your job is to write a proper E-Mail to the candidate from the organization with the job role, the candidate's name, organization name, and the body of this E-Mail should be in the above format."""}
        ]

        # Call OpenAI GPT-3.5-turbo
        chat_completion = client.chat.completions.create(
            model = "GPT-3",
            messages = conversation,
            max_tokens=700,
            temperature=0
        )

        response = chat_completion.choices[0].message.content

        result += response + "\n-------------------------------------------------------------------------------------\n"

      return result
    
    def clear(self,jobDescription,resume,result_email):
      jobDescription = None
      resume = None
      result_email = None
      return jobDescription, resume, result_email    
    
    def gradio_interface(self):
      with gr.Blocks(css="style.css",theme='freddyaboulton/test-blue') as app:
            gr.HTML("""<center><h1 class ="center" style="color:#fff"></h1></center>
            <br><center><h1 style="color:#fff">Candidate Assessment and Communication</h1></center>""")
            with gr.Row(elem_id="col-container"):
              with gr.Column(scale=0.55, min_width=150, ):
                jobDescription = gr.File(label="Job Description", file_types = [".pdf",".txt"])
              with gr.Column(scale=0.55, min_width=150):
                resume = gr.File(label="Resume", file_types = [".pdf",".txt"] , file_count="multiple")
            with gr.Row(elem_id="col-container"):
              with gr.Column(scale=0.80, min_width=150):
                analyse = gr.Button("Analyse")
              with gr.Column(scale=0.20, min_width=150):
                clear_btn = gr.ClearButton()                  
            with gr.Row(elem_id="col-container"):
              with gr.Column(scale=1.0, min_width=150):
                result_email = gr.Textbox(label="E-mail", lines=10)
    
            analyse.click(self.responce_from_ai, [jobDescription, resume], [result_email])
            clear_btn.click(self.clear,[jobDescription,resume,result_email],[jobDescription,resume,result_email] )
    
      app.launch()

if __name__ == "__main__":
    resume = ResumeAnalyser()
    answer = resume.gradio_interface()