update env2

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+streamlit
+torch
+torchvision
+Pillow
+requests

src/streamlit_app.py

@@ -1,40 +1,192 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import torch
+import json
+import requests
+import os
+from torchvision import models, transforms
+from PIL import Image
+from urllib.request import urlretrieve
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# --- ATUR PATH MODEL DAN LABEL (gunakan direktori yang dapat ditulis di Hugging Face Spaces) ---
+BASE_DIR = "/tmp/streamlit_app"
+
+# Pastikan STREAMLIT_HOME berada di direktori yang dapat ditulis
+os.environ["STREAMLIT_HOME"] = BASE_DIR
+MODEL_DIR = os.path.join(BASE_DIR, "models")
+LABELS_DIR = os.path.join(BASE_DIR, "labels")
+
+os.makedirs(MODEL_DIR, exist_ok=True)
+os.makedirs(LABELS_DIR, exist_ok=True)
+
+MODEL_FILENAME = os.getenv("MODEL_FILENAME","mobilenetv2.pth")
+LABELS_FILENAME = os.getenv("LABELS_FILENAME", "labels.json")
+
+model_path = os.path.join(MODEL_DIR, MODEL_FILENAME)
+labels_path = os.path.join(LABELS_DIR, LABELS_FILENAME)
+
+MODEL_URL = os.getenv("MODEL_URL","https://download.pytorch.org/models/mobilenet_v2-b0353104.pth")
+LABELS_URL = os.getenv("LABELS_URL", "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json")
+
+# --- KONFIGURASI APLIKASI ---
+st.set_page_config(
+    page_title="Klasifikasi Gambar (PyTorch) 📸",
+    page_icon="🖼️",
+    layout="centered"
+)
+
+# --- FUNGSI-FUNGSI ---
+@st.cache_resource
+def load_model():
+    """Memuat model MobileNetV2 dari file lokal atau mengunduh jika belum ada."""
+    if not os.path.exists(model_path):
+        st.info("Mengunduh model MobileNetV2...")
+        try:
+            urlretrieve(MODEL_URL, model_path)
+            st.success("Model berhasil diunduh.")
+        except Exception as e:
+            st.error(f"Gagal mengunduh model: {str(e)}")
+            return None
+
+    try:
+        # Buat model tanpa weight
+        model = models.mobilenet_v2(weights=None)
+        # Muat state_dict dari file lokal
+        state_dict = torch.load(model_path, map_location=torch.device('cpu'))
+        model.load_state_dict(state_dict)
+        model.eval()
+        return model
+    except Exception as e:
+        st.error(f"Gagal memuat model: {str(e)}")
+        return None
+
+
+
+@st.cache_data
+def load_labels():
+    """Memuat label dari file lokal atau mengunduh jika belum ada."""
+    if not os.path.exists(labels_path):
+        st.info("Mengunduh label ImageNet...")
+        try:
+            response = requests.get(LABELS_URL)
+            response.raise_for_status()
+            with open(labels_path, 'w') as f:
+                json.dump(response.json(), f)
+            st.success("Label berhasil diunduh.")
+        except Exception as e:
+            st.error(f"Gagal mengunduh label: {str(e)}")
+            return None
+
+    try:
+        with open(labels_path, 'r') as f:
+            labels = json.load(f)
+        return labels
+    except Exception as e:
+        st.error(f"Gagal memuat label: {str(e)}")
+        return None
+
+
+
+def preprocess_image(image):
+    """Melakukan pra-pemrosesan gambar agar sesuai dengan input model PyTorch."""
+    try:
+        # Definisikan transformasi
+        preprocess = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
+        # Terapkan transformasi dan tambahkan dimensi batch
+        img_t = preprocess(image)
+        batch_t = torch.unsqueeze(img_t, 0)
+        return batch_t
+    except Exception as e:
+        st.error(f"Gagal memproses gambar: {str(e)}")
+        return None
+
+def predict(image, model, labels):
+    """Melakukan prediksi klasifikasi pada gambar."""
+    try:
+        st.info("🧠 Model sedang menganalisis gambar...")
+
+        # Pra-pemrosesan gambar
+        batch_t = preprocess_image(image)
+        if batch_t is None:
+            return None
+
+        # Lakukan prediksi tanpa menghitung gradien
+        with torch.no_grad():
+            output = model(batch_t)
+
+        # Dapatkan probabilitas dengan softmax
+        probabilities = torch.nn.functional.softmax(output[0], dim=0)
+
+        # Dapatkan 3 kelas dengan probabilitas tertinggi
+        top3_prob, top3_catid = torch.topk(probabilities, 3)
+
+        # Siapkan hasil
+        results = []
+        for i in range(top3_prob.size(0)):
+            class_name = labels[top3_catid[i]]
+            probability = top3_prob[i].item()
+            results.append((class_name, probability))
+
+        return results
+    except Exception as e:
+        st.error(f"Gagal melakukan prediksi: {str(e)}")
+        return None
+
+# --- TAMPILAN UTAMA APLIKASI ---
+
+st.title("🖼️ Aplikasi Klasifikasi Gambar (PyTorch)")
+st.write(
+    "Unggah sebuah gambar, dan AI akan mencoba menebak objek apa yang ada di dalamnya! "
+    "Aplikasi ini menggunakan model **MobileNetV2** dari PyTorch."
+)
+
+# Muat model dan label
+try:
+    model = load_model()
+    labels = load_labels()
+
+    if model is None or labels is None:
+        st.error("Aplikasi tidak dapat dijalankan karena gagal memuat model atau label.")
+        st.stop()
+except Exception as e:
+    st.error(f"Kesalahan saat inisialisasi aplikasi: {str(e)}")
+    st.stop()
+
+# Komponen untuk unggah file
+uploaded_file = st.file_uploader(
+    "Pilih sebuah gambar...",
+    type=["jpg", "jpeg", "png"],
+    help="Format file yang didukung: JPG, JPEG, PNG"
+)
+
+if uploaded_file is not None:
+    try:
+        # Buka dan tampilkan gambar yang diunggah
+        image = Image.open(uploaded_file).convert('RGB')
+        st.image(image, caption='Gambar yang Anda Unggah', use_column_width=True)
+
+        # Tombol untuk memulai klasifikasi
+        if st.button('✨ Klasifikasikan Gambar Ini!'):
+            with st.spinner('Tunggu sebentar...'):
+                # Lakukan prediksi
+                predictions = predict(image, model, labels)
+
+                if predictions is not None:
+                    st.subheader("✅ Hasil Prediksi Teratas:")
+                    for i, (label, score) in enumerate(predictions):
+                        st.write(f"{i+1}. **{label.replace('_', ' ').title()}** - Keyakinan: {score:.2%}")
+                else:
+                    st.error("Prediksi gagal. Silakan coba lagi atau unggah gambar lain.")
+    except Exception as e:
+        st.error(f"Kesalahan saat memproses gambar yang diunggah: {str(e)}")
+        # Tambahan debugging untuk membantu identifikasi
+        st.write("Detail error: Periksa koneksi internet atau format gambar.")
+
+st.divider()
+st.markdown(
+    "Dibuat dengan ❤️ menggunakan [Streamlit](https://streamlit.io), [PyTorch](https://pytorch.org/) & [Hugging Face Spaces](https://huggingface.co/spaces)."
+)