Update app.py

app.py

@@ -8,43 +8,11 @@ from PyPDF2 import PdfReader
 import gradio as gr
 from PIL import Image
 
+# Ensure you get the API key from environment variables
 api_key = os.environ.get("CEREBRAS_API_KEY")
-md_converter = MarkItDown()
-
-def extract_file_preview(file_path):
-    """
-    Extracts a preview of the file based on its format.
-    """
-    try:
-        file_ext = os.path.splitext(file_path)[-1].lower()
-
-        if file_ext in [".jpg", ".jpeg", ".png"]:
-            return Image.open(file_path)
 
-        elif file_ext == ".pdf":
-            reader = PdfReader(file_path)
-            return "\n".join([page.extract_text() for page in reader.pages[:2]])
-
-        elif file_ext in [".docx"]:
-            doc = Document(file_path)
-            return "\n".join([para.text for para in doc.paragraphs[:20]])
-
-        elif file_ext in [".pptx"]:
-            ppt = Presentation(file_path)
-            slides_text = []
-            for slide in ppt.slides[:5]:
-                slide_text = []
-                for shape in slide.shapes:
-                    if hasattr(shape, "text"):
-                        slide_text.append(shape.text)
-                slides_text.append("\n".join(slide_text))
-            return "\n---\n".join(slides_text)
-
-        else:
-            return "File preview not supported for this format."
-
-    except Exception as e:
-        return f"Error extracting file preview: {str(e)}"
+# Initialize MarkItDown instance
+md_converter = MarkItDown()
 
 # Functions for resume optimization
 def create_prompt(resume_string: str, jd_string: str) -> str:
@@ -101,13 +69,7 @@ You are a professional resume optimization expert specializing in tailoring resu
 """
 
 def get_resume_response(prompt: str, api_key: str, model: str = "llama-3.3-70b", temperature: float = 0.7) -> str:
-    """
-    Sends a resume optimization prompt to Cerebras' API and returns the optimized resume response.
-    """
-    # Initialize the Cerebras client with the API key
     client = Cerebras(api_key=api_key)
-
-    # Make API call using the Llama 3.3 70B model
     stream = client.chat.completions.create(
         messages=[
             {"role": "system", "content": "Expert resume writer"},
@@ -120,91 +82,76 @@ def get_resume_response(prompt: str, api_key: str, model: str = "llama-3.3-70b",
         top_p=1
     )
 
-    # Collect the response chunks from the stream
     response_string = ""
     for chunk in stream:
         response_string += chunk.choices[0].delta.content or ""
-
     return response_string
 
 def process_resume(file, jd_string):
+    # Initialize MarkItDown
+    md = MarkItDown()
     try:
-        file_path = file.name
-        original_preview = extract_file_preview(file_path)
+        # Convert file to Markdown
+        original_md = md.convert(file.name)
+        original_text = original_md.text_content
+
+        # Save the original Markdown to a file
+        original_file_path = "resumes/original_resume.md"
+        with open(original_file_path, "w", encoding="utf-8") as f:
+            f.write(original_text)
 
-        result = md_converter.convert(file_path)
-        resume_string = result.text_content
+        # Create optimization prompt
+        prompt = create_prompt(original_text, jd_string)
+        response_string = get_resume_response(prompt, api_key)
 
-        prompt = f"Optimize resume based on job description: {jd_string}"
-        optimized_resume = "Optimized resume placeholder"  # Simulate response for now.
+        # Extract optimized resume and suggestions
+        response_list = response_string.split("## Additional Suggestions")
+        new_resume = response_list[0]
 
-        # Save the files for download
-        original_file_path = file_path
+        # Save the optimized Markdown to a file
         optimized_file_path = "resumes/optimized_resume.md"
         with open(optimized_file_path, "w", encoding="utf-8") as f:
-            f.write(optimized_resume)
-
-        return original_preview, resume_string, optimized_resume, original_file_path, optimized_file_path
-    except Exception as e:
-        return f"Error processing file: {str(e)}", "", "", "", ""
+            f.write(new_resume)
 
-def export_as_pdf(resume_md):
-    try:
-        pdf_path = "resumes/optimized_resume.pdf"
-        HTML(string=resume_md).write_pdf(pdf_path)
-        return pdf_path
+        return original_text, new_resume, "resumes/original_resume.md", "resumes/optimized_resume.md"
     except Exception as e:
-        return f"Failed to export PDF: {str(e)}"
+        return f"Error processing file: {str(e)}", "", None, None
 
-def export_as_word(resume_md):
+def export_resume(new_resume):
     try:
-        doc_path = "resumes/optimized_resume.docx"
-        doc = Document()
-        for line in resume_md.split("\n"):
-            doc.add_paragraph(line)
-        doc.save(doc_path)
-        return doc_path
+        # Save optimized Markdown as PDF
+        output_pdf_file = "resumes/resume_new.pdf"
+        html_content = new_resume
+        HTML(string=html_content).write_pdf(output_pdf_file, stylesheets=['resumes/style.css'])
+        return f"Successfully exported resume to {output_pdf_file} 🎉"
     except Exception as e:
-        return f"Failed to export Word: {str(e)}"
+        return f"Failed to export resume: {str(e)} 💔"
 
-# Gradio UI
+# Gradio App
 with gr.Blocks() as app:
     gr.Markdown("# Resume Optimizer 📄")
     gr.Markdown("Upload your resume, paste the job description, and get actionable insights!")
 
     with gr.Row():
-        resume_input = gr.File(label="Upload Your Resume")
-        jd_input = gr.Textbox(label="Paste Job Description", lines=5)
+        resume_input = gr.File(label="Upload Your Resume")    
+        jd_input = gr.Textbox(label="Paste the Job Description Here", lines=9, interactive=True, placeholder="Paste job description...")
+
+    run_button = gr.Button("Optimize Resume 🤖")
 
-    run_button = gr.Button("Optimize Resume")
-    
     with gr.Row():
-        before_preview = gr.Markdown(label="Original File Preview")
-        before_md = gr.Markdown(label="Original (Markdown)")
-        after_md = gr.Markdown(label="Optimized (Markdown)")
+        before_md = gr.Markdown(label="Original Resume (Before)")
+        after_md = gr.Markdown(label="Optimized Resume (After)")
+        output_suggestions = gr.Markdown(label="Suggestions")
 
     with gr.Row():
-        download_before = gr.File(label="Download Original")
-        download_after_md = gr.File(label="Download Optimized (Markdown)")
-        download_after_pdf = gr.File(label="Download Optimized (PDF)")
-        download_after_word = gr.File(label="Download Optimized (Word)")
-
-    run_button.click(
-        process_resume,
-        inputs=[resume_input, jd_input],
-        outputs=[before_preview, before_md, after_md, download_before, download_after_md]
-    )
-    
-    gr.Button("Export as PDF").click(
-        export_as_pdf,
-        inputs=[after_md],
-        outputs=[download_after_pdf]
-    )
-    
-    gr.Button("Export as Word").click(
-        export_as_word,
-        inputs=[after_md],
-        outputs=[download_after_word]
-    )
+        download_before = gr.File(label="Download Original Resume")
+        download_after = gr.File(label="Download Optimized Resume")
+        
+    export_button = gr.Button("Export Optimized Resume as PDF 🚀")
+    export_result = gr.Markdown(label="Export Result")
+
+    # Bindings
+    run_button.click(process_resume, inputs=[resume_input, jd_input], outputs=[before_md, after_md, download_before, download_after, output_suggestions])
+    export_button.click(export_resume, inputs=[after_md], outputs=[export_result])
 
 app.launch()