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
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+
+# Initialize Groq client with your API key
+key = "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"
+client = Groq(api_key=key)
+
+# Function to read PDF files
+def read_pdf(file_path):
+    text = extract_text(file_path)
+    return text
+
+# Function to read DOCX files
+def read_docx(file_path):
+    doc = Document(file_path)
+    text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
+    return text
+
+# Function to analyze CV for role fit using Llama 3.3 and Groq API
+def analyze_cv_with_llama(cv_text, job_description):
+    completion = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "You are a hiring expert with extensive experience in talent acquisition. "
+                    "Analyze the following CV content against a job description and score over 100 in these areas: "
+                    "Relevance to the job, Work experience, Skills, Educational background, Achievements and Impact, "
+                    "and Format and Presentation. Provide detailed scores and feedback for each area."
+                )
+            },
+            {"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,
+        stop=None,
+    )
+    # Collect and return the analysis response
+    analysis_text = ''.join([chunk.message.content for chunk in completion.choices])
+    return analysis_text
+
+# Function to generate a PDF with analysis results
+def generate_pdf(content, output_path):
+    pdf = canvas.Canvas(output_path, pagesize=letter)
+    pdf.setFont("Helvetica", 10)
+    width, height = letter
+    y = height - 40  # Start position
+    
+    for line in content.split("\n"):
+        pdf.drawString(40, y, line)
+        y -= 15  # Move down for the next line
+        if y < 40:  # Create a new page if space is insufficient
+            pdf.showPage()
+            pdf.setFont("Helvetica", 10)
+            y = height - 40
+    
+    pdf.save()
+
+# Function to handle file upload and job screening
+def process_file(file, job_description):
+    # Save the uploaded file to a temporary location
+    file_path = file.name
+    file_extension = os.path.splitext(file_path)[1].lower()
+
+    # Read the file content based on its format
+    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
+
+    # Analyze the CV against the job description
+    analysis_result = analyze_cv_with_llama(cv_text, job_description)
+
+    # Generate PDF with analysis results
+    output_pdf_path = "cv_analysis_result.pdf"
+    generate_pdf(analysis_result, output_pdf_path)
+
+    return analysis_result, output_pdf_path
+
+# Gradio Interface
+def main():
+    with gr.Blocks() as iface:
+        with gr.Row():
+            file_input = gr.File(label="Upload CV/Resume (PDF or DOCX)")
+            job_description_input = gr.Textbox(label="Enter Job Description", lines=5, placeholder="Type the job description here...")
+
+        analysis_output = gr.Markdown(label="CV Screening Analysis")
+        download_button = gr.File(label="Download Analysis as PDF")
+        
+        def process_input(file, job_description):
+            if not file or not job_description.strip():
+                return "Please upload a file and provide a job description.", None
+            return process_file(file, job_description)
+        
+        submit_button = gr.Button("Analyze CV")
+        submit_button.click(process_input, [file_input, job_description_input], [analysis_output, download_button])
+
+    iface.launch(share=True)
+
+if __name__ == "__main__":
+    main()