Create app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+import numpy as np
+import json
+
+# Load your JSON data
+with open("data.json") as f:
+    data = json.load(f)
+
+# Initialize model
+model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# Precompute embeddings for all file names
+file_names = [item["file_name"] for item in data]
+file_embeddings = model.encode(file_names)
+
+app = FastAPI()
+
+class Query(BaseModel):
+    text: str
+
+@app.post("/search")
+async def search(query: Query):
+    # Encode query
+    query_embedding = model.encode([query.text])
+    
+    # Compute cosine similarity
+    similarities = np.dot(file_embeddings, query_embedding.T).flatten()
+    
+    # Find best match
+    best_match_idx = np.argmax(similarities)
+    
+    return {
+        "best_match": data[best_match_idx]["file_name"],
+        "similarity_score": float(similarities[best_match_idx])
+    }