Create app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from typing import List
+from sentence_transformers import SentenceTransformer
+from sklearn.metrics.pairwise import cosine_similarity
+import numpy as np
+
+# -----------------------
+# Load model ONCE (important for performance)
+# -----------------------
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+app = FastAPI(
+    title="CV Matching API",
+    description="Rank CVs against a Job Description using BERT embeddings",
+    version="1.0"
+)
+
+# -----------------------
+# Request schema
+# -----------------------
+class MatchRequest(BaseModel):
+    job_description: str
+    cvs: List[str]
+
+# -----------------------
+# Response schema
+# -----------------------
+class CVScore(BaseModel):
+    cv_text: str
+    relevance_score: float
+
+class MatchResponse(BaseModel):
+    results: List[CVScore]
+
+# -----------------------
+# Utility: text cleaning (optional but recommended)
+# -----------------------
+def clean_text(text: str) -> str:
+    return text.replace("\n", " ").strip().lower()
+
+# -----------------------
+# API endpoint
+# -----------------------
+@app.post("/match", response_model=MatchResponse)
+def match_cvs(request: MatchRequest):
+    # Clean input
+    jd = clean_text(request.job_description)
+    cvs = [clean_text(cv) for cv in request.cvs]
+
+    # Embed job description
+    jd_embedding = model.encode([jd])
+
+    # Embed CVs
+    cv_embeddings = model.encode(cvs)
+
+    # Compute cosine similarity
+    scores = cosine_similarity(jd_embedding, cv_embeddings)[0]
+
+    # Build response
+    results = []
+    for cv_text, score in zip(request.cvs, scores):
+        results.append(
+            CVScore(
+                cv_text=cv_text,
+                relevance_score=float(score)
+            )
+        )
+
+    # Sort by relevance (descending)
+    results.sort(key=lambda x: x.relevance_score, reverse=True)
+
+    return MatchResponse(results=results)