app.py

from flask import Flask, render_template, request, send_from_directory
from db import get_db_connection
from deepface import DeepFace
import json
import os
import numpy as np

app = Flask(__name__)

UPLOAD_FOLDER = "uploads"
os.makedirs(UPLOAD_FOLDER, exist_ok=True)

# ✅ THIS ROUTE IS REQUIRED (VERY IMPORTANT)
@app.route("/uploads/<filename>")
def uploaded_file(filename):
    return send_from_directory(UPLOAD_FOLDER, filename)

# Home page
@app.route("/", methods=["GET", "POST"])
def index():
    if request.method == "POST":
        file = request.files.get("photo")

        if not file:
            return render_template("index.html")

        filename = file.filename
        filepath = os.path.join(UPLOAD_FOLDER, filename)
        file.save(filepath)

        # Face detection + embedding
        try:
            result = DeepFace.represent(
                img_path=filepath,
                model_name="Facenet",
                enforce_detection=True
            )
            query_embedding = np.array(result[0]["embedding"])
        except Exception:
            return render_template(
                "results.html",
                error="❌ No face detected. Please upload a clear face image.",
                matches=[],
                uploaded_filename=None
            )

        # Fetch embeddings from DB
        conn = get_db_connection()
        cursor = conn.cursor()
        cursor.execute("SELECT image_name, embedding FROM face_embeddings")
        db_results = cursor.fetchall()
        conn.close()

        similarities = []
        for name, emb_json in db_results:
            db_emb = np.array(json.loads(emb_json))
            sim = np.dot(query_embedding, db_emb) / (
                np.linalg.norm(query_embedding) * np.linalg.norm(db_emb)
            )

            similarities.append({
                "name": name,
                "similarity": float(sim),
                "image_url": f"/static/dataset/{name}"
            })

        similarities.sort(key=lambda x: x["similarity"], reverse=True)
        top_matches = similarities[:5]

        return render_template(
            "results.html",
            matches=top_matches,
            uploaded_filename=filename,
            error=None
        )

    return render_template("index.html")


if __name__ == "__main__":
    app.run(debug=True)