Create app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import config
+import openai
+
+# Set your OpenAI API key
+openai.api_key = config.OPENAI_API_KEY
+
+jd_summary_global = ""  # Global variable to store the job description summary
+
+def process_jd(text):
+    global jd_summary_global  # Declare the global variable
+    if not text.strip():  # Check if the text is empty or contains only whitespace
+        jd_summary_global = "No JD"  # Update the global variable
+        return "No JD"
+    
+    try:
+        # Structuring a prompt to ask GPT-3.5 to summarize the job description
+        prompt = f"Summarize the following job description into its job nature, responsibilities, and requirements:\n\n{text}"
+        
+        # Uploading text to OpenAI 
+        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
+        jd_summary = response['choices'][0]['message']['content'].strip()
+        jd_summary_global = jd_summary  # Update the global variable
+        return jd_summary
+    except Exception as e:
+        return str(e)
+
+def cv_rating(cv_data):
+    global jd_summary_global  # Declare the global variable
+    if len(jd_summary_global) <= 1 or jd_summary_global == "No JD":  
+        return "No JD in the previous tab."
+    if len(cv_data) <= 1:
+        return "No CV data"
+    try:
+        # Construct a prompt to ask GPT-3.5 to rate the CV based on the job description summary
+        prompt = f"""
+        Job Description Summary: {jd_summary_global}
+        CV Data: {cv_data}
+        
+        Rate the compatibility of the CV with the job description and provide strengths, weaknesses, and recommendations to strengthen the CV.
+        """
+        # Uploading text to OpenAI 
+        response = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt}])
+        return response['choices'][0]['message']['content'].strip()
+    except Exception as e:
+        return str(e)
+    
+jd_sum = gr.Interface(
+    fn=process_jd,  # function to process the text
+    inputs=gr.Textbox(lines=30, label="Job Description"),
+    outputs=gr.Textbox(lines=30, label="JD Summary", show_copy_button=True),
+    live=False,
+    title="Job Description Summarizer",
+    description="An app to summarize job descriptions into job nature, responsibilities, and requirements.",
+)
+
+cv_rate_interface = gr.Interface(
+    fn=cv_rating,
+    inputs=gr.Textbox(lines=30, label="CV Data", placeholder="Paste the CV data here"),
+    outputs=gr.Textbox(lines=30, label="ATS Rating System", show_copy_button=True),
+    live=False,
+    title="CV Rating",
+    description="An app to rate CV compatibility with job description, providing strengths, weaknesses, and recommendations.",
+)
+bespokecv = gr.TabbedInterface([jd_sum, cv_rate_interface],tab_names=['Job Description Summarizer','CV ATS Rating'])
+
+if __name__ == "__main__":
+    bespokecv.launch(share=True)