master to main

.gitattributes

@@ -1 +1,35 @@
-lsp_som_model.pkl filter=lfs diff=lfs merge=lfs -text
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,13 +0,0 @@
-FROM python:3.12-slim
-
-WORKDIR /app
-
-COPY requirements.txt .
-
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY . .
-
-EXPOSE 8000
-
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Makefile

@@ -1,9 +0,0 @@
-run:
-	docker run -p 8001:8000 --name lsp-container lsp-api
-
-clean:
-	docker stop lsp-container
-	docker rm lsp-container
-
-build:
-	docker build -t lsp-api .

lsp_som_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2d4587aac55f7871f1638e8b5fa1b698de7d94b91578ff5d6730c2f216485117
-size 7436886

main.py

@@ -1,78 +0,0 @@
-import pickle
-import numpy as np
-import io
-import math
-from fastapi import FastAPI, File, UploadFile, HTTPException
-from PIL import Image
-
-app = FastAPI(
-    title="Peruvian Sign Language (LSP) Recognition API",
-    description="Sube una imagen de una seña del alfabeto de la LSP para predecir la letra correspondiente usando un Mapa Autoorganizado (SOM).",
-    version="1.0.0"
-)
-
-try:
-    with open('lsp_som_model.pkl', 'rb') as f:
-        model_data = pickle.load(f)
-    som = model_data['som']
-    label_map = model_data['label_map']
-    CLASSES = model_data['classes'] # La lista ['A', 'B', 'C', ...]
-    IMG_SIZE = model_data['img_size'] # El tamaño de la imagen
-    print("✅ Modelo y activos cargados exitosamente.")
-    print(f"    - Clases reconocidas: {CLASSES}")
-    print(f"    - Tamaño de imagen esperado: {IMG_SIZE}x{IMG_SIZE}")
-except FileNotFoundError:
-    print("❌ ERROR: No se encontró el archivo del modelo 'lsp_som_model.pkl'.")
-    som = None
-
-def preprocess_image_from_bytes(image_bytes: bytes):
-    try:
-        img = Image.open(io.BytesIO(image_bytes)).convert('L') # 'L' para escala de grises
-        img = img.resize((IMG_SIZE, IMG_SIZE))
-        img_array = np.array(img)
-        img_normalized = img_array / 255.0
-        return img_normalized.flatten()
-    except Exception as e:
-        raise HTTPException(status_code=400, detail=f"Archivo de imagen inválido. Error: {e}")
-
-@app.get("/", tags=["Status"])
-def read_root():
-    return {"status": "ok", "message": "API de Reconocimiento de LSP!!"}
-
-@app.post("/predict", tags=["Prediction"])
-async def predict_sign(file: UploadFile = File(..., description="Un archivo de imagen de una seña de la LSP.")):
-    if not som:
-        raise HTTPException(status_code=503, detail="El modelo no está cargado.")
-
-    image_bytes = await file.read()
-
-    feature_vector = preprocess_image_from_bytes(image_bytes)
-
-    winner_neuron = som.winner(feature_vector)
-
-    predicted_index = label_map.get(winner_neuron, -1)
-
-    # Vecino mas cercano para prediccion
-    is_best_guess = False
-    if predicted_index == -1:
-        is_best_guess = True
-        min_dist = float('inf')
-        for mapped_pos, mapped_label in label_map.items():
-            dist = math.sqrt((winner_neuron[0] - mapped_pos[0])**2 + (winner_neuron[1] - mapped_pos[1])**2)
-            if dist < min_dist:
-                min_dist = dist
-                predicted_index = mapped_label
-    
-    if predicted_index != -1:
-        predicted_letter = CLASSES[predicted_index]
-        prediction_type = "Nearest Neighbor" if is_best_guess else "Direct Match"
-    else:
-        predicted_letter = "Unknown"
-        prediction_type = "Error (No Mapped Neurons Found)"
-
-    return {
-        "filename": file.filename,
-        "predicted_letter": predicted_letter,
-        "prediction_type": prediction_type,
-        "winner_neuron_on_map": [int(coord) for coord in winner_neuron]
-    }

requirements.txt

@@ -1,6 +0,0 @@
-fastapi
-uvicorn[standard]
-python-multipart
-minisom
-numpy
-Pillow