added cover letter generator and interview Q&A functionalities

app.py

@@ -27,6 +27,8 @@ def process_jd(text):
 
 def cv_rating(cv_data):
     global jd_summary_global  # Declare the global variable
+    global cv_rating_global
+    
     if len(jd_summary_global) <= 1 or jd_summary_global == "No JD":  
         return "No JD in the previous tab."
     if len(cv_data) <= 1:
@@ -41,17 +43,59 @@ def cv_rating(cv_data):
         """
         # 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}])
+        cv_rating_global= response['choices'][0]['message']['content'].strip()
+        return cv_rating_global
+    except Exception as e:
+        return str(e)
+
+def create_cover_letter(additional_info):
+    global jd_summary_global  # Declare the global variable
+    global cv_rating_global
+    if len(jd_summary_global) <= 1 or jd_summary_global == "No JD":  
+        return "No JD in the previous tab."
+    if len(cv_rating_global) <= 1:
+        return "No CV data"
+    try:
+        # Constructing a prompt for GPT-3.5 to create a tailored cover letter
+        prompt = f"""
+        Job Description: {jd_summary_global}
+        CV Data: {cv_rating_global}
+        Additional Information: {additional_info}
+
+        Create a tailored cover letter based on the job description and CV data provided:
+        """
+        # 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)
-    
+
+def interview_qa(additional_info):
+    global cv_rating_global
+    if len(cv_rating_global) <= 1:
+        return "No CV data"
+    try:
+        # Constructing a prompt for GPT-3.5 to create interview questions and answers
+        prompt = f"""
+        CV Data: {cv_rating_global}
+        Additional Information: {additional_info}
+
+        Generate at least 10 interview questions and provide potential answers based on the CV data and additional information provided:
+        """
+        # 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", placeholder="Paste the job description here"),
+    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.",
+    api_name="jd_sum"
 )
 
 cv_rate_interface = gr.Interface(
@@ -61,8 +105,30 @@ cv_rate_interface = gr.Interface(
     live=False,
     title="CV Rating",
     description="An app to rate CV compatibility with job description, providing strengths, weaknesses, and recommendations.",
+    api_name="cv_rate_interface"
+)
+
+cover_letter_interface = gr.Interface(
+    fn=create_cover_letter,
+    inputs=[gr.Textbox(lines=10, label="Additional Information", placeholder="Add any additional information or preferences for your cover letter here")],
+    outputs=gr.Textbox(lines=30, label="Output", show_copy_button=True),
+    live=False,
+    title="Cover Letter Creator",
+    description="An app to create a tailored cover letter based on job description and CV data. You may input additional information in the additional information box to add highlight specific experiences/projects and/or skills.",
+    api_name="cover_letter_interface"
 )
-bespokecv = gr.TabbedInterface([jd_sum, cv_rate_interface],tab_names=['Job Description Summarizer','CV ATS Rating'])
+
+interview_qa_interface = gr.Interface(
+    fn=interview_qa,
+    inputs=[gr.Textbox(lines=10, label="Additional Information", placeholder="Add any specific questions or additional information here")],
+    outputs=gr.Textbox(lines=30, label="Output", show_copy_button=True),
+    live=False,
+    title="Interview Q&A",
+    description="An app to generate interview questions and answers based on CV data and additional information.",
+    api_name="interview_qa"
+)
+
+bespokecv = gr.TabbedInterface([jd_sum, cv_rate_interface,cover_letter_interface,interview_qa_interface],tab_names=['Job Description Summarizer','CV ATS Rating','Cover Letter Generator','Interview Q&A'])
 
 if __name__ == "__main__":
     bespokecv.launch(share=True)