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.dockerignore

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+.env
+.venv
+__pycache__
+*.py[cod]
+.pytest_cache
+.mypy_cache
+.ruff_cache
+.ipynb_checkpoints
+Dockerfile

.env.example

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+AI_ENGINE_ALLOWED_ORIGINS=http://localhost:3000

.gitattributes

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-*.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
+*.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
+cnn_fis_classifier.keras filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+*.lcov
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi/*
+!.pixi/config.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule*
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+# Temporary file for partial code execution
+tempCodeRunnerFile.py
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml

.python-version

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+3.12

Dockerfile

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+# syntax=docker/dockerfile:1.7
+
+FROM ghcr.io/astral-sh/uv:python3.12-bookworm-slim AS runtime
+
+ENV PYTHONUNBUFFERED=1 \
+    PYTHONDONTWRITEBYTECODE=1 \
+    TF_CPP_MIN_LOG_LEVEL=2 \
+    UV_COMPILE_BYTECODE=1 \
+    UV_LINK_MODE=copy
+
+WORKDIR /app
+
+# Runtime libraries needed by OpenCV/TensorFlow wheels on Debian slim.
+RUN apt-get update \
+    && apt-get install -y --no-install-recommends \
+        libgl1 \
+        libglib2.0-0 \
+        libgomp1 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-dev
+
+COPY main.py inference.py ./
+COPY artifacts ./artifacts
+
+RUN useradd --create-home --shell /usr/sbin/nologin appuser \
+    && chown -R appuser:appuser /app
+
+USER appuser
+
+EXPOSE 7860
+
+HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
+    CMD ./.venv/bin/python -c "import urllib.request; urllib.request.urlopen('http://127.0.0.1:7860/health', timeout=5).read()" || exit 1
+
+CMD ["./.venv/bin/uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860", "--proxy-headers"]

README.md

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----
-title: Soft Computing Ai
-emoji: 👁
-colorFrom: purple
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# AI Engine
+
+FastAPI inference service for the Soft Computing final project.
+
+This service:
+
+- accepts an image upload through an HTTP API,
+- loads the trained Keras CNN model,
+- preprocesses the image using the same `256x256` RGB normalization flow as the notebook,
+- runs the CNN prediction,
+- applies the same Fuzzy Inference System rules from the notebook, and
+- returns the final label plus the raw class probabilities.
+
+## Model Path
+
+The service reads the trained model from:
+
+```text
+ai_engine/artifacts/cnn_fis_classifier.keras
+```
+
+## Install Dependencies
+
+```powershell
+cd ai_engine
+uv sync
+```
+
+## Run the API
+
+```powershell
+cd ai_engine
+uv run uvicorn main:app --reload --host 0.0.0.0 --port 8000
+```
+
+The API will be available at:
+
+```text
+http://127.0.0.1:8000
+```
+
+## Endpoints
+
+### `GET /health`
+
+Returns service status plus model path information.
+
+### `POST /predict`
+
+Accepts one multipart file field named `file`.
+
+Example with PowerShell:
+
+```powershell
+curl.exe -X POST "http://127.0.0.1:8000/predict" `
+  -F "file=@C:\path\to\image.jpg"
+```
+
+Example response:
+
+```json
+{
+  "label": "Positive",
+  "fis_score": 82.34,
+  "cnn_prediction_name": "Positive",
+  "probabilities": {
+    "negative": 0.07,
+    "neutral": 0.11,
+    "positive": 0.82
+  }
+}
+```
+
+## CORS
+
+By default, the API allows requests from:
+
+- `http://localhost:3000`
+- `http://127.0.0.1:3000`
+
+To override that list:
+
+```powershell
+$env:AI_ENGINE_ALLOWED_ORIGINS="http://localhost:3000,http://127.0.0.1:3000"
+```

app.py

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+from __future__ import annotations
+
+import os
+from contextlib import asynccontextmanager
+
+import uvicorn
+from fastapi import FastAPI, File, HTTPException, UploadFile
+from fastapi.concurrency import run_in_threadpool
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+from inference import get_model_path, load_model, predict_image_bytes
+
+
+class ProbabilityResponse(BaseModel):
+    negative: float
+    neutral: float
+    positive: float
+
+
+class PredictionResponse(BaseModel):
+    label: str
+    fis_score: float
+    cnn_prediction_name: str
+    probabilities: ProbabilityResponse
+
+
+def get_allowed_origins() -> list[str]:
+    configured_origins = os.getenv("AI_ENGINE_ALLOWED_ORIGINS", "")
+    if configured_origins.strip():
+        return [origin.strip() for origin in configured_origins.split(",") if origin.strip()]
+
+    return [
+        "http://localhost:3000",
+        "http://127.0.0.1:3000",
+    ]
+
+
+@asynccontextmanager
+async def lifespan(_: FastAPI):
+    yield
+
+
+app = FastAPI(
+    title="Soft Computing AI Engine",
+    description="FastAPI service for CNN + FIS image sentiment inference.",
+    version="0.1.0",
+    lifespan=lifespan,
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=get_allowed_origins(),
+    allow_credentials=False,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+@app.get("/health")
+def health():
+    model_path = get_model_path()
+    return {
+        "status": "ok",
+        "model_exists": model_path.exists(),
+        "model_loaded": load_model.cache_info().currsize > 0,
+        "model_path": str(model_path),
+    }
+
+
+@app.post("/predict", response_model=PredictionResponse)
+async def predict(file: UploadFile = File(...)):
+    if not file.content_type or not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="Upload harus berupa file gambar.")
+
+    image_bytes = await file.read()
+    await file.close()
+
+    if not image_bytes:
+        raise HTTPException(status_code=400, detail="File gambar kosong.")
+
+    try:
+        prediction = await run_in_threadpool(predict_image_bytes, image_bytes)
+    except FileNotFoundError as error:
+        raise HTTPException(status_code=500, detail=str(error)) from error
+    except ValueError as error:
+        raise HTTPException(status_code=400, detail=str(error)) from error
+    except Exception as error:
+        raise HTTPException(
+            status_code=500,
+            detail=f"Prediksi gagal dijalankan: {error}",
+        ) from error
+
+    return PredictionResponse.model_validate(prediction)
+
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

best_hyperparameters.json

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+{
+  "learning_rate": 0.003,
+  "filters_1": 96,
+  "filters_2": 256,
+  "dense_units": 64,
+  "dropout_rate": 0.5,
+  "batch_size": 16
+}

cnn_fis_classifier.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:38b5984a674788bba461685adf41cf58199ebb30d30706010a2d53c8c311deb4
+size 10029074

inference.py

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+from __future__ import annotations
+
+from functools import lru_cache
+from pathlib import Path
+from typing import Any
+
+import cv2
+import numpy as np
+import skfuzzy as fuzz
+import tensorflow as tf
+from skfuzzy import control as ctrl
+
+IMAGE_SIZE = (256, 256)
+LABEL_TOKENS = ("neg", "neutral", "pos")
+LABEL_MAP = {label: index for index, label in enumerate(LABEL_TOKENS)}
+LABEL_NAMES = {
+    LABEL_MAP["neg"]: "Negative",
+    LABEL_MAP["neutral"]: "Neutral",
+    LABEL_MAP["pos"]: "Positive",
+}
+NUM_CLASSES = len(LABEL_TOKENS)
+MODEL_PATH = (
+    Path(__file__).resolve().parent / "artifacts" / "cnn_fis_classifier.keras"
+)
+
+
+def get_model_path() -> Path:
+    return MODEL_PATH.resolve()
+
+
+@lru_cache(maxsize=1)
+def load_model():
+    model_path = get_model_path()
+    if not model_path.exists():
+        raise FileNotFoundError(
+            f"Keras model tidak ditemukan di: {model_path}. "
+            "Pastikan file artefak ada di folder ai_engine/artifacts."
+        )
+
+    return tf.keras.models.load_model(model_path)
+
+
+def preprocess_image_bytes(image_bytes: bytes) -> np.ndarray:
+    image_array = np.frombuffer(image_bytes, dtype=np.uint8)
+    image = cv2.imdecode(image_array, cv2.IMREAD_COLOR)
+    if image is None:
+        raise ValueError("Gambar tidak bisa dibaca. Pastikan file upload adalah gambar yang valid.")
+
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    image = cv2.resize(image, IMAGE_SIZE, interpolation=cv2.INTER_AREA)
+    image = image.astype(np.float32) / 255.0
+    return image
+
+
+def normalize_probability_vector(model_output: np.ndarray) -> np.ndarray:
+    probabilities = np.asarray(model_output, dtype=np.float32).reshape(-1)
+
+    if probabilities.shape[0] == NUM_CLASSES:
+        return probabilities
+
+    if probabilities.shape[0] == 1:
+        # Current artifact is binary sigmoid: one score means positive confidence.
+        positive_probability = float(np.clip(probabilities[0], 0.0, 1.0))
+        return np.asarray(
+            [
+                1.0 - positive_probability,
+                0.0,
+                positive_probability,
+            ],
+            dtype=np.float32,
+        )
+
+    raise ValueError(
+        f"Expected either {NUM_CLASSES} class probabilities or 1 binary probability, "
+        f"received shape {probabilities.shape}."
+    )
+
+
+def probability_dict_from_vector(probability_vector: np.ndarray) -> dict[str, float]:
+    probabilities = normalize_probability_vector(probability_vector)
+    if probabilities.shape[0] != NUM_CLASSES:
+        raise ValueError(
+            f"Expected {NUM_CLASSES} class probabilities, received shape {probabilities.shape}."
+        )
+
+    return {
+        "negative": float(probabilities[LABEL_MAP["neg"]]),
+        "neutral": float(probabilities[LABEL_MAP["neutral"]]),
+        "positive": float(probabilities[LABEL_MAP["pos"]]),
+    }
+
+
+def build_fuzzy_system() -> ctrl.ControlSystemSimulation:
+    positive_confidence = ctrl.Antecedent(np.linspace(0, 1, 101), "positive_confidence")
+    neutral_confidence = ctrl.Antecedent(np.linspace(0, 1, 101), "neutral_confidence")
+    negative_confidence = ctrl.Antecedent(np.linspace(0, 1, 101), "negative_confidence")
+    final_decision = ctrl.Consequent(np.linspace(0, 100, 101), "final_decision")
+
+    for variable in (positive_confidence, neutral_confidence, negative_confidence):
+        variable["low"] = fuzz.trimf(variable.universe, [0.0, 0.0, 0.45])
+        variable["medium"] = fuzz.trimf(variable.universe, [0.2, 0.5, 0.8])
+        variable["high"] = fuzz.trimf(variable.universe, [0.55, 1.0, 1.0])
+
+    final_decision["negative"] = fuzz.trimf(final_decision.universe, [0, 0, 35])
+    final_decision["neutral"] = fuzz.trimf(final_decision.universe, [30, 50, 70])
+    final_decision["positive"] = fuzz.trimf(final_decision.universe, [65, 100, 100])
+
+    rules = [
+        ctrl.Rule(
+            positive_confidence["high"]
+            & neutral_confidence["low"]
+            & negative_confidence["low"],
+            final_decision["positive"],
+        ),
+        ctrl.Rule(
+            positive_confidence["high"]
+            & neutral_confidence["medium"]
+            & negative_confidence["low"],
+            final_decision["positive"],
+        ),
+        ctrl.Rule(
+            positive_confidence["medium"]
+            & neutral_confidence["low"]
+            & negative_confidence["low"],
+            final_decision["positive"],
+        ),
+        ctrl.Rule(
+            negative_confidence["high"]
+            & positive_confidence["low"]
+            & neutral_confidence["low"],
+            final_decision["negative"],
+        ),
+        ctrl.Rule(
+            negative_confidence["high"]
+            & positive_confidence["low"]
+            & neutral_confidence["medium"],
+            final_decision["negative"],
+        ),
+        ctrl.Rule(
+            negative_confidence["medium"]
+            & positive_confidence["low"]
+            & neutral_confidence["low"],
+            final_decision["negative"],
+        ),
+        ctrl.Rule(
+            neutral_confidence["high"]
+            & positive_confidence["low"]
+            & negative_confidence["low"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            neutral_confidence["high"]
+            & positive_confidence["medium"]
+            & negative_confidence["low"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            neutral_confidence["high"]
+            & negative_confidence["medium"]
+            & positive_confidence["low"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            positive_confidence["medium"] & negative_confidence["medium"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            positive_confidence["medium"] & neutral_confidence["medium"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            negative_confidence["medium"] & neutral_confidence["medium"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            positive_confidence["high"] & negative_confidence["high"],
+            final_decision["neutral"],
+        ),
+        ctrl.Rule(
+            positive_confidence["low"]
+            & negative_confidence["low"]
+            & neutral_confidence["low"],
+            final_decision["neutral"],
+        ),
+    ]
+
+    control_system = ctrl.ControlSystem(rules)
+    return ctrl.ControlSystemSimulation(control_system)
+
+
+def fuzzy_label_from_probabilities(probability_vector: np.ndarray) -> tuple[str, float]:
+    probabilities = probability_dict_from_vector(probability_vector)
+
+    fuzzy_system = build_fuzzy_system()
+    fuzzy_system.input["positive_confidence"] = probabilities["positive"]
+    fuzzy_system.input["neutral_confidence"] = probabilities["neutral"]
+    fuzzy_system.input["negative_confidence"] = probabilities["negative"]
+    fuzzy_system.compute()
+
+    decision_score = float(fuzzy_system.output["final_decision"])
+    if decision_score >= 65:
+        label = "Positive"
+    elif decision_score <= 35:
+        label = "Negative"
+    else:
+        label = "Neutral"
+
+    return label, decision_score
+
+
+def predict_image_bytes(image_bytes: bytes) -> dict[str, Any]:
+    image = preprocess_image_bytes(image_bytes)
+    model = load_model()
+
+    batch = np.expand_dims(image, axis=0).astype(np.float32)
+    model_output = np.asarray(model.predict(batch, verbose=0)[0], dtype=np.float32)
+    probability_vector = normalize_probability_vector(model_output)
+    probabilities = probability_dict_from_vector(probability_vector)
+    fuzzy_label, fuzzy_score = fuzzy_label_from_probabilities(probability_vector)
+    cnn_prediction_name = LABEL_NAMES[int(np.argmax(probability_vector))]
+
+    return {
+        "label": fuzzy_label,
+        "fis_score": fuzzy_score,
+        "cnn_prediction_name": cnn_prediction_name,
+        "probabilities": probabilities,
+    }

pyproject.toml

@@ -0,0 +1,18 @@
+[project]
+name = "ai-engine"
+version = "0.1.0"
+description = "FastAPI inference service for the Soft Computing final project"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "fastapi>=0.116.0",
+    "numpy>=2.4.5",
+    "opencv-python>=4.13.0.92",
+    "python-multipart>=0.0.20",
+    "scikit-fuzzy>=0.5.0",
+    "tensorflow[and-cuda]>=2.21.0; sys_platform == 'linux'",
+    "tensorflow>=2.21.0; sys_platform == 'win32'",
+    "uvicorn[standard]>=0.35.0",
+    "scipy>=1.17.1",
+    "networkx>=3.6.1",
+]