Update app.py

app.py

@@ -8,15 +8,13 @@ from langchain_groq import ChatGroq
 from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
 from dotenv import load_dotenv
-load_dotenv()
-
 import os
+import tempfile
+
+load_dotenv()
 groq_api_key = os.getenv("GROQ_API_KEY")
 os.environ["GROQ_API_KEY"] = groq_api_key
 
-import os
-import tempfile
-
 def extract_text_from_pdf(pdf_file):
     with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp:
         temp.write(pdf_file)
@@ -61,7 +59,7 @@ def generate_skill_gap_report(user_skills, job_skills, missing_skills, match_per
 
 def process_skill_gap(resume_pdf, jd_pdf):
     if resume_pdf is None or jd_pdf is None:
-        return "❌ Please upload both Resume and Job Description PDFs.", "", "", ""
+        return 0, "❌ Please upload both Resume and Job Description PDFs.", "", "", ""
 
     resume_text = extract_text_from_pdf(resume_pdf)
     jd_text = extract_text_from_pdf(jd_pdf)
@@ -78,23 +76,34 @@ def process_skill_gap(resume_pdf, jd_pdf):
 
     report = generate_skill_gap_report(user_skills, job_skills, missing_skills, match_percent)
 
-    return f"✅ Skill Match: {match_percent}%", f"❌ Missing Skills: {', '.join(missing_skills) if missing_skills else 'None'}", f"🔎 Similarity Score: {similarity_percent}%", report
-
-demo = gr.Interface(
-    fn=process_skill_gap,
-    inputs=[
-        gr.File(label="Upload Resume (PDF)", type="binary"),
-        gr.File(label="Upload Job Description (PDF)", type="binary")
-    ],
-    outputs=[
-        gr.Textbox(label="Skill Match Percentage"),
-        gr.Textbox(label="Missing Skills"),
-        gr.Textbox(label="Similarity Score"),
-        gr.Textbox(label="AI-Generated Skill Gap Report")
-    ],
-    title="📄 Skill Gap AI Checker (Gradio + LangChain + Groq LLaMA3)",
-    description="Upload your Resume PDF and Job Description PDF to analyze your skill match percentage, missing skills, and get an AI-generated report using Groq's LLaMA3 model."
-)
-
-if __name__ == "__main__":
-    demo.launch()
+    skill_text = f"✅ Skill Match: {match_percent}%"
+    missing_text = f"❌ Missing Skills: {', '.join(missing_skills) if missing_skills else 'None'}"
+    similarity_text = f"🔎 Resume vs JD Similarity Score: {similarity_percent}%"
+
+    return match_percent, skill_text, missing_text, similarity_text, report
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 AI Skill Gap Checker")
+    gr.Markdown("Upload your **Resume PDF** and **Job Description PDF**. The AI will analyze your skill match and generate an improvement report.")
+
+    with gr.Row():
+        resume_file = gr.File(label="📄 Upload Resume (PDF)", type="binary")
+        jd_file = gr.File(label="📄 Upload Job Description (PDF)", type="binary")
+
+    submit_btn = gr.Button("🚀 Analyze Skill Gap")
+
+    gr.Markdown("## 🎯 Match Percentage")
+    match_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Job Match Percentage (%)", interactive=False)
+
+    skill_match_text = gr.Textbox(label="Skill Match Details", interactive=False)
+    missing_skills_text = gr.Textbox(label="Missing Skills", interactive=False)
+    similarity_text = gr.Textbox(label="Resume-JD Similarity Score", interactive=False)
+    report_output = gr.Textbox(label="AI-Generated Skill Gap Report", lines=10, interactive=False)
+
+    submit_btn.click(
+        fn=process_skill_gap,
+        inputs=[resume_file, jd_file],
+        outputs=[match_slider, skill_match_text, missing_skills_text, similarity_text, report_output]
+    )
+
+demo.launch()