menghilangkan fast api hugging face tidak support di app.py

README.md

@@ -3,9 +3,10 @@ title: HistoryLens
 emoji: 🐢
 colorFrom: purple
 colorTo: red
-sdk: fastapi
+sdk: gradio
+sdk_version: 5.33.0
 app_file: app.py
 pinned: false
 license: apache-2.0
 short_description: Classification Image
----
+---

app.py

@@ -1,6 +1,6 @@
 import os
-os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # Disable all GPUs
-os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'  # Optional: disable oneDNN
+os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
 os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
 
@@ -10,36 +10,12 @@ tf.config.set_visible_devices([], 'GPU')
 import gradio as gr
 import numpy as np
 from tensorflow.keras.preprocessing import image
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from io import BytesIO
+from tensorflow.keras.models import model_from_json
 from PIL import Image
-import logging
-from tensorflow.keras.models import load_model, model_from_json
-from tensorflow.keras import mixed_precision
-from tensorflow.keras.saving import get_custom_objects, register_keras_serializable
-from tensorflow.keras.mixed_precision import Policy
 
-# Import deskripsi dan lokasi
 from description import description
 from location import location
 
-# @register_keras_serializable(package="keras")
-# class DTypePolicy(Policy):
-#     pass
-
-# from tensorflow.keras.saving import get_custom_objects
-# get_custom_objects()["DTypePolicy"] = DTypePolicy
-
-# Nonaktifkan GPU (jika tidak digunakan)
-tf.config.set_visible_devices([], 'GPU')
-
-# Inisialisasi logger
-# logging.basicConfig(level=logging.INFO)
-# logger = logging.getLogger(__name__)
-
-# ========== Fungsi Load Model dari File JSON + H5 ==========
 def load_model_from_file(json_path, h5_path):
     with open(json_path, "r") as f:
         json_config = f.read()
@@ -47,18 +23,14 @@ def load_model_from_file(json_path, h5_path):
     model.load_weights(h5_path)
     return model
 
-# ========== Load Model ==========
 model = load_model_from_file("model.json", "my_model.h5")
 
-
-# Daftar label
 labels = [
     "Benteng Vredeburg", "Candi Borobudur", "Candi Prambanan", "Gedung Agung Istana Kepresidenan",
     "Masjid Gedhe Kauman", "Monumen Serangan 1 Maret", "Museum Gunungapi Merapi",
     "Situs Ratu Boko", "Taman Sari", "Tugu Yogyakarta"
 ]
 
-# Fungsi klasifikasi
 def classify_image(img):
     try:
         img = img.resize((224, 224))
@@ -96,52 +68,16 @@ def classify_image(img):
     except Exception as e:
         return "Error", str(e), "-"
 
-# Fungsi untuk membuat FastAPI app
-def create_app():
-    app = FastAPI()
-    app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # atau daftar domain yang sah
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
+interface = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload Gambar"),
+    outputs=[
+        gr.Textbox(label="Output Klasifikasi"),
+        gr.Textbox(label="Deskripsi Lengkap", lines=20, max_lines=50),
+        gr.HTML(label="Link Lokasi"),
+    ],
+    title="Klasifikasi Gambar",
+    description="Upload gambar, sistem akan mengklasifikasikan dan memberikan deskripsi mengenai gambar tersebut."
 )
 
-
-
-    # @app.post("/api/predict")
-    # async def predict(file: UploadFile = File(...)):
-    #     contents = await file.read()
-    #     img = Image.open(BytesIO(contents)).convert("RGB")
-    #     label_output, deskripsi, lokasi, akurasi = classify_image(img)
-    #     return JSONResponse(content={
-    #         "label_output": label_output,
-    #         "deskripsi": deskripsi,
-    #         "lokasi": lokasi,
-    #         "confidence": akurasi
-    #     })
-
-    gradio_app = gr.Interface(
-        fn=classify_image,
-        inputs=gr.Image(type="pil", label="Upload Gambar"),
-        # outputs=["text", "text", "html"],
-        outputs=[
-            gr.Textbox(label="Output Klasifikasi"),
-            gr.Textbox(label="Deskripsi Lengkap", lines=20, max_lines=50),
-            gr.HTML(label="Link Lokasi"),
-        ],
-        # flagging_mode="never",
-        title="Klasifikasi Gambar",
-        description="Upload gambar, sistem akan mengklasifikasikan dan memberikan deskripsi mengenai gambar tersebut."
-    )
-
-    app = gr.mount_gradio_app(app, gradio_app, path="/gradio")
-    return app
-
-app = create_app()
-
-# Run server jika dijalankan langsung
-# if __name__ == "__main__":
-#     import uvicorn
-#     # app = create_app()
-#     uvicorn.run(app, host="127.0.0.1", port=8000)
+interface.launch()