Create app.py

app.py

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+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import gradio as gr
+
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+def get_embedding(text):
+    return model.encode([text])[0]
+
+def compute_similarity(resume, jd):
+    resume_emb = get_embedding(resume)
+    jd_emb = get_embedding(jd)
+    similarity = cosine_similarity([resume_emb], [jd_emb])[0][0]
+    return round(similarity * 100, 2)
+
+def check_resume(resume_text, jd_text):
+    score = compute_similarity(resume_text, jd_text)
+    result = f"Similarity Score: {score}%\n\n"
+    if score > 75:
+        result += "✅ Resume is highly aligned with the Job Description."
+    elif score > 50:
+        result += "⚠️ Resume is partially aligned. Consider refining keywords and skills."
+    else:
+        result += "❌ Resume is not aligned. May contain irrelevant or fake claims."
+    return result
+
+gr.Interface(
+    fn=check_resume,
+    inputs=[
+        gr.Textbox(label="Paste Resume Text", lines=20, placeholder="Copy your resume content here..."),
+        gr.Textbox(label="Paste Job Description", lines=20, placeholder="Copy the job description here...")
+    ],
+    outputs=gr.Textbox(label="Analysis Result"),
+    title="🧾 Resume-JD Alignment Detector",
+    description="Check if your resume genuinely matches the job description using LLM-based similarity."
+).launch()