Create app.py

app.py

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+import os
+import gradio as gr
+from pdfminer.high_level import extract_text
+from docx import Document
+from groq import Groq
+
+key = os.getenv("GroqCVs")
+client = Groq(api_key=key)
+
+# Function to read PDF files
+def read_pdf(file_path):
+    return extract_text(file_path)
+
+# Function to read DOCX files
+def read_docx(file_path):
+    doc = Document(file_path)
+    return "\n".join([paragraph.text for paragraph in doc.paragraphs])
+
+# Function to optimize CV using Llama 3.1 and Groq API
+def optimize_cv_with_llama(cv_text, job_description):
+    try:
+        # Compose the prompt
+        system_message = (
+            "You are an experienced Human Resource expert with 30 years of experience working for major international companies. "
+            "Your task is to optimize the following CV or Resume based on the provided job description. Tailor the CV content to align "
+            "with the job requirements and highlight relevant skills, experiences, and achievements. Present the optimized CV in Markdown format. "
+            "Ensure the formatting is professional and avoid adding extra comments."
+        )
+
+        # Send the job description and CV text to the Groq API
+        completion = client.chat.completions.create(
+            model="llama-3.3-70b-versatile",
+            messages=[
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": f"Job Description:\n{job_description}\n\nCV:\n{cv_text}"}
+            ],
+            temperature=0.7,
+            max_tokens=2048,
+            top_p=0.9,
+            stream=False,
+        )
+
+        # Return the optimized Markdown text
+        return ''.join([chunk.message.content for chunk in completion.choices])
+    except Exception as e:
+        return f"Error optimizing CV: {e}"
+
+# Function to save optimized Markdown as DOCX
+from markdown2 import markdown
+from bs4 import BeautifulSoup
+
+def save_markdown_to_docx(markdown_text, output_docx_path="optimized_cv.docx"):
+    # Convert Markdown to HTML
+    html_content = markdown(markdown_text)
+
+    # Parse HTML with BeautifulSoup
+    soup = BeautifulSoup(html_content, "html.parser")
+
+    # Create a new DOCX document
+    doc = Document()
+
+    # Iterate through HTML elements and map them to DOCX formatting
+    for element in soup.descendants:
+        if element.name == "h1":
+            para = doc.add_paragraph(element.text, style="Heading 1")
+        elif element.name == "h2":
+            para = doc.add_paragraph(element.text, style="Heading 2")
+        elif element.name == "h3":
+            para = doc.add_paragraph(element.text, style="Heading 3")
+        elif element.name == "ul":  # Unordered list
+            for li in element.find_all("li"):
+                doc.add_paragraph(li.text, style="List Bullet")
+        elif element.name == "ol":  # Ordered list
+            for li in element.find_all("li"):
+                doc.add_paragraph(li.text, style="List Number")
+        elif element.name == "p":  # Paragraph
+            doc.add_paragraph(element.text)
+        elif element.name == "strong":  # Bold text
+            para = doc.add_paragraph()
+            run = para.add_run(element.text)
+            run.bold = True
+        elif element.name == "em":  # Italics
+            para = doc.add_paragraph()
+            run = para.add_run(element.text)
+            run.italic = True
+        elif element.name == "a":  # Hyperlink
+            para = doc.add_paragraph(element.text)  # Add the text of the hyperlink
+            # Add the URL as well, optionally
+            para.add_run(f" ({element.get('href')})").italic = True
+
+    # Save the document
+    doc.save(output_docx_path)
+    return output_docx_path
+
+# Function to process file upload and optimization
+def process_inputs(cv_file, job_description):
+    try:
+        # Extract text from uploaded file
+        file_path = cv_file.name
+        file_extension = os.path.splitext(file_path)[1].lower()
+        if file_extension == ".pdf":
+            cv_text = read_pdf(file_path)
+        elif file_extension == ".docx":
+            cv_text = read_docx(file_path)
+        else:
+            return "Unsupported file format", None
+
+        # Optimize the CV based on the job description
+        optimized_markdown = optimize_cv_with_llama(cv_text, job_description)
+
+        # Save the optimized Markdown as DOCX
+        output_docx_path = "optimized_cv.docx"
+        save_markdown_to_docx(optimized_markdown, output_docx_path)
+
+        return optimized_markdown, output_docx_path
+    except Exception as e:
+        return f"Error processing inputs: {e}", None
+
+# Gradio Interface
+def main():
+    with gr.Blocks() as app:
+        # Title and Description
+        gr.Markdown(
+            """
+            # **AI-Powered CV Optimizer**
+            Optimize your CV or Resume to match job descriptions using the power of AI.
+            This application processes your uploaded CV along with a job description and generates a tailored, professional CV ready for submission.
+            """
+        )
+
+        # Input components
+        file_input = gr.File(label="Upload Your CV (PDF or DOCX)")
+
+
+        job_description = gr.Textbox(
+            label="Job Description",
+            placeholder="Paste the job description here...",
+            lines=5
+        )
+
+
+        # Output components
+        markdown_output = gr.Markdown(label="Optimized CV Preview")
+        download_button = gr.File(label="Download Optimized CV (DOCX)")
+
+        # Button to process inputs
+        run_button = gr.Button("Optimize CV")
+
+        # Define the interaction logic
+        def handle_inputs(cv_file, job_description):
+            markdown_text, docx_path = process_inputs(cv_file, job_description)
+            return markdown_text, docx_path
+
+        run_button.click(
+            handle_inputs,
+            inputs=[file_input, job_description],
+            outputs=[markdown_output, download_button]
+        )
+
+    app.launch(debug=True)
+
+if __name__ == "__main__":
+    main()