Update resume_generation_gemini_pro.py

resume_generation_gemini_pro.py

@@ -31,7 +31,7 @@ Original file is located at
 
 import docx2txt
 import PyPDF2
-
+import subprocess
 def extract_text(file_path):
     if file_path.endswith(".docx"):
         # Extract text from DOCX file
@@ -53,11 +53,10 @@ def extract_text(file_path):
 # auth.authenticate_user()
 
 import os
-
-GOOGLE_APPLICATION_CREDENTIALS = os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
-private_key_id = os.environ.get('PRIVATE_KEY_ID')
-private_key = os.environ.get('PRIVATE_KEY')
-client_id = os.environ.get('CLIENT_ID')
+# GOOGLE_APPLICATION_CREDENTIALS = os.environ["GOOGLE_APPLICATION_CREDENTIALS"]
+# private_key_id = os.environ.get('PRIVATE_KEY_ID')
+# private_key = os.environ.get('PRIVATE_KEY')
+# client_id = os.environ.get('CLIENT_ID')
 
 # !pip install python-docx
 
@@ -85,6 +84,10 @@ def save_resume_to_docx(tailored_resume, file_path):
     doc.add_paragraph(tailored_resume)
     doc.save(file_path)
 
+def save_resume_to_pdf(docx_file_path, file_path):
+    subprocess.run(['libreoffice', '--headless', '--convert-to', 'pdf', '--outdir', file_path, docx_file_path])
+
+
 # Function to read text from a .docx file
 def read_docx(file_path):
     doc = Document(file_path)
@@ -122,8 +125,6 @@ Using the candidate's resume above and the job description below, create a tailo
 [Job Description Start]
 {job_description}
 [Job Description End]
-
-
 Please generate a resume that:
 1. Uses real data from the candidate's resume, including name, and education.
 2. Avoids placeholders like "[Your Name]" and includes actual details. This is important.
@@ -193,24 +194,33 @@ def convert_resume_to_word(markdown_text,output_file):
     print(f"Markdown converted and saved as {output_file}")
 
 #Entry function for the model
-def generate_gemini(current_resume,job_description):
+def generate_gemini(current_resume,job_description , download_path , doctype):
     st.header('Resume Tailoring')
-
     # Load the resume and job description from Google Drive
     resume_text = extract_text(current_resume)
     job_description = extract_text(job_description)
 
     # Tailor resume based on job description
     tailored_resume = tailor_resume(resume_text, job_description)
-    output_file = f"Tailored_Resume_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx"
-    convert_resume_to_word(tailored_resume,output_file)
-
-    st.success(f"Tailored resume saved to {output_file}")
-    output_text = extract_text(output_file)
-    st.write(output_text)
-
-    return tailored_resume, output_file        
-
+    st.write("**Tailored Resume:**")
+    st.write(tailored_resume)
+    print(tailored_resume)
+
+        # Save the tailored resume to a .docx file
+    if tailored_resume:
+        time = datetime.now().strftime('%Y%m%d_%H%M%S')
+        file_name = f"Tailored_Resume_{time}.docx"
+        file_path = os.path.join(download_path , file_name)
+        convert_resume_to_word(tailored_resume,file_path)
+        if(doctype == 'pdf'):
+            file_name = f"Tailored_Resume_{time}.pdf"
+            save_resume_to_pdf(file_path, download_path)
+            file_path = os.path.join(download_path , file_name)
+
+        st.success(f"Download tailored resume")
+        # st.success(f"Tailored resume saved to {file_path}")        
+
+    return tailored_resume, file_path
 
 # Main function for Streamlit app
 # def Gemini_pro_main(current_resume,job_description):