Update app.py

app.py

@@ -5,19 +5,16 @@ import io
 from docx import Document
 import os
 import pymupdf  # Corrected import for PyMuPDF
-# For PDF generation
 from reportlab.pdfgen import canvas
 from reportlab.lib.pagesizes import letter
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
 from reportlab.lib.styles import getSampleStyleSheet
 from reportlab.lib import colors
 
-# Initialize Hugging Face Inference Client with Meta-Llama-3.1-8B-Instruct
 client = InferenceClient(
     model="meta-llama/Meta-Llama-3-8B-Instruct",
     token=os.getenv("HF_TOKEN"))
 
-# Function to extract text from PDF
 def extract_text_from_pdf(pdf_file):
     try:
         pdf_document = pymupdf.open(pdf_file)
@@ -26,7 +23,6 @@ def extract_text_from_pdf(pdf_file):
     except Exception as e:
         return f"Error reading PDF: {e}"
 
-# Function to extract text from DOCX
 def extract_text_from_docx(docx_file):
     try:
         doc = Document(docx_file)
@@ -35,127 +31,85 @@ def extract_text_from_docx(docx_file):
     except Exception as e:
         return f"Error reading DOCX: {e}"
 
-# Function to analyze CV
+def chatbot_response(message, history):
+    try:
+        response = client.text_generation(message, max_new_tokens=512)
+        history.append((message, response))
+        return "", history  # Return empty input and updated history
+    except Exception as e:
+        return "Error: Unable to generate response.", history
+
 def parse_cv(file, job_description):
     if file is None:
         return "Please upload a CV file.", ""
     try:
         file_path = file.name
         file_ext = os.path.splitext(file_path)[1].lower()
-        if file_ext == ".pdf":
-            extracted_text = extract_text_from_pdf(file_path)
-        elif file_ext == ".docx":
-            extracted_text = extract_text_from_docx(file_path)
-        else:
-            return "Unsupported file format. Please upload a PDF or DOCX file.", ""
+        extracted_text = extract_text_from_pdf(file_path) if file_ext == ".pdf" else extract_text_from_docx(file_path)
     except Exception as e:
         return f"Error reading file: {e}", ""
     if extracted_text.startswith("Error"):
-        return extracted_text, "Error during text extraction. Please check the file."
-    prompt = (
-        f"Analyze the CV against the job description. Provide a summary, assessment, "
-        f"and a score 0-10.\n\n"
-        f"Job Description:\n{job_description}\n\n"
-        f"Candidate CV:\n{extracted_text}\n"
-    )
+        return extracted_text, "Error during text extraction."
+    prompt = f"Analyze this CV for job relevance.\nJob Description:\n{job_description}\n\nCV:\n{extracted_text}\n"
     try:
         analysis = client.text_generation(prompt, max_new_tokens=512)
-        return extracted_text, f"--- Analysis Report ---\n{analysis}"
+        return extracted_text, f"**Analysis Report:**\n{analysis}"
     except Exception as e:
         return extracted_text, f"Analysis Error: {e}"
 
-# Function to toggle the download button
-def toggle_download_button(analysis_report):
-    return gr.update(interactive=bool(analysis_report.strip()), visible=bool(analysis_report.strip()))
-
-# Function to create PDF report
 def create_pdf_report(report_text):
-    if not report_text.strip():
-        report_text = "No analysis report to convert."
-
     pdf_buffer = io.BytesIO()
     doc = SimpleDocTemplate(pdf_buffer, pagesize=letter)
     styles = getSampleStyleSheet()
-    Story = []
-
-    title = Paragraph("<b>Analysis Report</b>", styles['Title'])
-    Story.append(title)
-    Story.append(Spacer(1, 12))
-
-    report_paragraph = Paragraph(report_text.replace("\n", "<br/>"), styles['BodyText'])
-    Story.append(report_paragraph)
-
+    Story = [Paragraph("<b>Analysis Report</b>", styles['Title']), Spacer(1, 12)]
+    Story.append(Paragraph(report_text.replace("\n", "<br/>"), styles['BodyText']))
     doc.build(Story)
     pdf_buffer.seek(0)
-    return (pdf_buffer.getvalue(), "analysis_report.pdf")
+    return pdf_buffer.getvalue(), "analysis_report.pdf"
 
 def process_resume(resume_file, job_title):
-    """
-    Processes the uploaded resume, optimizes it for the given job title using the LLM,
-    and returns the optimized resume content.
-    """
     if resume_file is None:
         return "Please upload a resume file."
-
     try:
         file_path = resume_file.name
         file_ext = os.path.splitext(file_path)[1].lower()
-
-        if file_ext == ".pdf":
-            resume_text = extract_text_from_pdf(file_path)
-        elif file_ext == ".docx":
-            resume_text = extract_text_from_docx(file_path)
-        else:
-            return "Unsupported file format. Please upload a PDF or DOCX file."
-
+        resume_text = extract_text_from_pdf(file_path) if file_ext == ".pdf" else extract_text_from_docx(file_path)
         if resume_text.startswith("Error"):
             return resume_text
-
-        prompt = (
-            f"Optimize the following resume for the job title: {job_title}.\n"
-            f"Include relevant skills, experience, and keywords related to the job title.\n\n"
-            f"Resume:\n{resume_text}\n"
-        )
-
+        prompt = f"Optimize this resume for {job_title}:\n{resume_text}\n"
         optimized_resume = client.text_generation(prompt, max_new_tokens=1024)
-        return optimized_resume
-
+        return optimized_resume.replace("\n", "  \n")  # Ensure Markdown formatting
     except Exception as e:
         return f"Error processing resume: {e}"
-# Build the Gradio UI
+
 demo = gr.Blocks()
 with demo:
     gr.Markdown("## AI-powered CV Analyzer, Optimizer, and Chatbot")
+    
     with gr.Tab("Chatbot"):
         chat_interface = gr.ChatInterface(
-            lambda message, history: client.chat_completion(
-                messages=[{"role": "user", "content": message}],
-                max_tokens=512,
-            ),
-            chatbot=gr.Chatbot(label="Chatbot", type="messages"),
+            chatbot_response,
+            chatbot=gr.Chatbot(label="Chatbot"),
             textbox=gr.Textbox(placeholder="Enter your message here...", label="Message"),
         )
+    
     with gr.Tab("CV Analyzer"):
-        gr.Markdown("### Upload your CV and provide the job description")
         file_input = gr.File(label="Upload CV", file_types=[".pdf", ".docx"])
         job_desc_input = gr.Textbox(label="Job Description", lines=5)
         extracted_text = gr.Textbox(label="Extracted CV Content", lines=10, interactive=False)
-        analysis_output = gr.Textbox(label="Analysis Report", lines=10, interactive=False)
-        download_pdf_button = gr.Button("Download Analysis as PDF", visible=False, interactive=False)
+        analysis_output = gr.Markdown(label="Analysis Report")
+        download_pdf_button = gr.Button("Download Analysis as PDF", visible=False)
         pdf_file = gr.File(label="Download PDF", interactive=False)
         analyze_button = gr.Button("Analyze CV")
-
         analyze_button.click(parse_cv, [file_input, job_desc_input], [extracted_text, analysis_output])
-        analyze_button.click(toggle_download_button, [analysis_output], [download_pdf_button])
         download_pdf_button.click(create_pdf_report, [analysis_output], [pdf_file])
+    
     with gr.Tab("CV Optimizer"):
-        gr.Markdown("### Upload your Resume and Enter Job Title")
         resume_file = gr.File(label="Upload Resume (PDF or Word)", file_types=[".pdf", ".docx"])
-        job_title_input = gr.Textbox(label="Job Title", lines=1)
-        optimized_resume_output = gr.Textbox(label="Optimized Resume", lines=20)
+        job_title_input = gr.Textbox(label="Job Title")
+        optimized_resume_output = gr.Markdown(label="Optimized Resume")
         optimize_button = gr.Button("Optimize Resume")
-
         optimize_button.click(process_resume, [resume_file, job_title_input], [optimized_resume_output])
 
 if __name__ == "__main__":
-    demo.queue().launch()
+    demo.queue().launch()