Create app.py

app.py

@@ -0,0 +1,95 @@
+import os
+import openai
+import PyPDF2
+import gradio as gr
+import docx
+
+class QuestionsGenerator:
+    def __init__(self):
+        openai.api_key = os.getenv("OPENAI_API_KEY")
+
+    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 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 gradio_interface(self):
+      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="rightimage" align="right" src="https://companieslogo.com/img/orig/RAND.AS_BIG-0f1935a4.png?t=1651813778" alt="Image" width="210" height="210">""")
+
+            with gr.Row(elem_id="col-container"):
+              with gr.Column():
+                gr.HTML("<br>")
+                gr.HTML(
+                    """<h1 style="text-align:center; color:"white">Randstad Questions For Screening</h1> """
+                )
+                gr.HTML("<br>")
+              with gr.Column():
+                jobDescription = gr.File(label="Job Description")
+
+              with gr.Column():
+                analyse = gr.Button("Generate")
+
+              with gr.Column():
+                result = gr.Textbox(label="Questions For Screening",lines=8)
+
+            analyse.click(self.response, [jobDescription], result)
+
+      app.launch()
+
+ques = QuestionsGenerator()
+ques.gradio_interface()