Create app.py

app.py

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+import gradio as gr
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+
+def match_resume(resume, job1, job2, job3):
+    # Calculate similarity
+    resume_embedding = model.encode(resume, convert_to_tensor=True)
+
+    # Encode each job description using a loop
+    job_embeddings = []
+    for job in [job1, job2, job3]:
+        job_embeddings.append(model.encode(job, convert_to_tensor=True))
+
+    # Calculate the similarities using a loop
+    similarities = []
+    for job_emb in job_embeddings:
+        similarity = util.pytorch_cos_sim(resume_embedding, job_emb).item() * 100
+        similarities.append(similarity)
+
+    best_job_index = 1
+    best_match = similarities[0]
+
+    for i in range(1, len(similarities)):
+        if similarities[i] > best_match:
+            best_match = similarities[i]
+            best_job_index = i + 1
+
+
+    results = []
+    for i, sim in enumerate(similarities):
+      results.append(f"Job {i+1}: {sim:.2f}% match")
+
+    results.append(f"Best Match: Job {best_job_index} with {best_match:.2f}%")
+
+    return "\n".join(results)
+
+# Gradio interface
+interface = gr.Interface(fn=match_resume,
+                         inputs=["text", "text", "text", "text"],
+                         outputs="text",
+                             examples=[
+        ["Experienced software developer skilled in Python and AI.",
+         "Looking for a Python developer with experience in AI and ML.",
+         "Seeking a data scientist with expertise in deep learning.",
+         "Hiring a web developer proficient in JavaScript and React."],
+
+        ["Marketing specialist with a focus on digital campaigns.",
+         "Seeking a social media expert to manage brand presence.",
+         "Hiring a digital marketer with experience in SEO and PPC.",
+         "Looking for a content writer to create engaging blog posts."]
+    ]
+                         )
+
+interface.launch()