Create app.py

app.py

@@ -0,0 +1,179 @@
+import os
+import openai
+import PyPDF2
+import gradio as gr
+import docx
+import re
+import plotly.graph_objects as go
+
+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 show_file(self,file_path):
+      return file_path.name
+
+    def launch_gradio_interface(self, share: bool = True):
+        with gr.Blocks(css="style.css",theme='karthikeyan-adople/hudsonhayes-gray') as app:
+
+            with gr.Row():
+                with gr.Column(elem_id="col-container"):
+                    gr.HTML("""<center><h1>Resume</h1></center>""")
+                    file_output = gr.File(elem_classes="filenameshow")
+                    upload_button = gr.UploadButton(
+                        "Browse File",file_types=[".txt", ".pdf", ".doc", ".docx",".json",".csv"],
+                        elem_classes="filenameshow")
+                with gr.TabItem("Designation"):
+                    btn = gr.Button(value="Submit")
+                    output_text = gr.Textbox(label="Designation List")
+                with gr.TabItem("Summarized"):  
+                    analyse = gr.Button("Analyze")
+                    summary_result = gr.Textbox(label="Summarized",lines=8)      
+                with gr.TabItem("Skills and Education Gaps"):  
+                    analyse_resume = gr.Button("Analyze Resume")
+                    result = gr.Textbox(label="Skills and Education Gaps",lines=8)
+                with gr.TabItem("Course"): 
+                    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)
+
+        app.launch(debug=True)
+
+if __name__ == "__main__":
+    resume_overall = Resume_Overall()
+    resume_overall.launch_gradio_interface()