upload fruit captioning ai

.gitattributes

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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import numpy as np
+import pickle
+import tensorflow as tf
+import os
+import pandas as pd
+from datetime import datetime
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.applications.mobilenet_v2 import MobileNetV2, preprocess_input
+from tensorflow.keras.preprocessing.image import img_to_array
+from PIL import Image
+
+# ==========================================
+# 1. KONFIGURASI PATH (DINAMIS)
+# ==========================================
+# BASE_DIR mengarah ke folder tempat file .py ini berada
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+MODEL_DIR = os.path.join(BASE_DIR, 'models')
+
+# Path file di dalam folder models
+PATH_MODEL_CAPTION = os.path.join(MODEL_DIR, 'fruit_caption_model.h5')
+PATH_TOKENIZER = os.path.join(MODEL_DIR, 'tokenizer.pkl')
+PATH_GRAFIK = os.path.join(MODEL_DIR, '1_grafik_akurasi.png')
+PATH_LOG = os.path.join(MODEL_DIR, 'riwayat_penggunaan.csv')
+
+# ==========================================
+# 2. TAMPILAN UI UTAMA
+# ==========================================
+st.set_page_config(page_title="AI Fruit Captioning", page_icon="🥭", layout="wide")
+
+st.title("🍎 Identifikasi Citra Buah Menggunakan Image Captioning")
+st.write("Sistem otomatis yang mendeskripsikan jenis buah, kandungan vitamin, dan manfaatnya menggunakan Deep Learning.")
+
+# ==========================================
+# 3. FUNGSI LOGIKA SISTEM
+# ==========================================
+
+def save_to_log(nama, vitamin, deskripsi):
+    """Menyimpan hasil identifikasi ke file CSV internal."""
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    new_data = {
+        'Waktu': [now],
+        'Nama Buah': [nama],
+        'Vitamin': [vitamin],
+        'Deskripsi': [deskripsi]
+    }
+    df_new = pd.DataFrame(new_data)
+    
+    if not os.path.exists(MODEL_DIR):
+        os.makedirs(MODEL_DIR)
+        
+    if not os.path.isfile(PATH_LOG):
+        df_new.to_csv(PATH_LOG, index=False)
+    else:
+        df_new.to_csv(PATH_LOG, mode='a', header=False, index=False)
+
+@st.cache_resource
+def load_assets():
+    """Memuat model dan tokenizer sekali saja (caching)."""
+    if not os.path.exists(PATH_MODEL_CAPTION) or not os.path.exists(PATH_TOKENIZER):
+        raise FileNotFoundError("File model atau tokenizer tidak ditemukan di folder 'models'!")
+        
+    model = load_model(PATH_MODEL_CAPTION)
+    with open(PATH_TOKENIZER, 'rb') as f:
+        tokenizer = pickle.load(f)
+    
+    # Encoder: MobileNetV2
+    base_model = MobileNetV2(weights='imagenet')
+    fe_model = tf.keras.Model(inputs=base_model.inputs, outputs=base_model.layers[-2].output)
+    
+    return model, tokenizer, fe_model
+
+def generate_caption(image, model, tokenizer, fe_model):
+    """Proses ekstraksi fitur dan pembuatan teks deskripsi."""
+    img = image.convert('RGB')
+    img = img.resize((224, 224))
+    img = img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img = preprocess_input(img)
+    
+    # Ekstraksi Fitur (CNN)
+    feature = fe_model.predict(img, verbose=0)
+    
+    # Generate Teks (LSTM)
+    max_length = 21 
+    in_text = 'startseq'
+    
+    for i in range(max_length):
+        sequence = tokenizer.texts_to_sequences([in_text])[0]
+        sequence = pad_sequences([sequence], maxlen=max_length)
+        yhat = model.predict([feature, sequence], verbose=0)
+        idx = np.argmax(yhat)
+        word = tokenizer.index_word.get(idx)
+        if word is None or word == 'endseq':
+            break
+        in_text += ' ' + word
+    
+    return in_text.replace('startseq', '').strip()
+
+# ==========================================
+# 4. ALUR PROSES APLIKASI
+# ==========================================
+
+# Memuat Aset
+try:
+    model, tokenizer, fe_model = load_assets()
+except Exception as e:
+    st.error(f"⚠️ Gagal memuat sistem: {e}")
+    st.stop()
+
+# Input Gambar
+uploaded_file = st.file_uploader("Unggah foto buah (JPG/PNG/JPEG)", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    img_display = Image.open(uploaded_file).convert('RGB')
+    
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        st.subheader("🖼️ Citra Input")
+        st.image(img_display, use_container_width=True)
+        
+    with col2:
+        st.subheader("🤖 Hasil Analisis AI")
+        with st.spinner('Menganalisis citra...'):
+            try:
+                caption = generate_caption(img_display, model, tokenizer, fe_model)
+                
+                # Parsing teks (asumsi output: "buah [nama] deskripsi [teks] mengandung vitamin [vit]")
+                if ' deskripsi ' in caption and ' mengandung vitamin ' in caption:
+                    parts = caption.split(' deskripsi ')
+                    nama_buah = parts[0].replace('buah ', '').title()
+                    detail = parts[1].split(' mengandung vitamin ')
+                    deskripsi_clean = detail[0].capitalize()
+                    vitamin_clean = detail[1]
+                    
+                    # Tampilan Hasil ke User
+                    st.success(f"**Jenis Buah:** {nama_buah}")
+                    st.metric(label="Kandungan Utama", value=f"Vitamin {vitamin_clean}")
+                    st.info(f"**Keterangan:**\n{deskripsi_clean}")
+                    
+                    # Simpan ke Log Internal
+                    save_to_log(nama_buah, vitamin_clean, deskripsi_clean)
+                    st.toast("Data riwayat telah diperbarui!", icon="✅")
+                else:
+                    st.warning("Model memberikan output yang tidak standar:")
+                    st.write(f"_{caption}_")
+                    
+            except Exception as e:
+                st.error(f"Terjadi kesalahan saat klasifikasi: {e}")
+
+# ==========================================
+# 5. SIDEBAR (METODOLOGI & DOWNLOAD USER)
+# ==========================================
+with st.sidebar:
+    st.header("📊 Informasi & Riwayat")
+    
+    # Visualisasi Akurasi (Jika ada)
+    if os.path.exists(PATH_GRAFIK):
+        st.image(PATH_GRAFIK, caption="Grafik Performa Model", use_container_width=True)
+    
+    st.markdown("---")
+    st.subheader("📥 Download Riwayat")
+    st.write("Unduh hasil identifikasi Anda untuk keperluan laporan.")
+    
+    if os.path.exists(PATH_LOG):
+        df_log = pd.read_csv(PATH_LOG)
+        
+        # Tombol Download untuk User
+        csv_data = df_log.to_csv(index=False).encode('utf-8')
+        st.download_button(
+            label="Download File CSV",
+            data=csv_data,
+            file_name=f"riwayat_identifikasi_{datetime.now().strftime('%Y%m%d')}.csv",
+            mime="text/csv",
+            use_container_width=True
+        )
+        
+        # Tampilkan tabel singkat di sidebar
+        st.write("**5 Data Terakhir:**")
+        st.dataframe(df_log.tail(5), use_container_width=True)
+    else:
+        st.info("Belum ada data riwayat.")
+
+    st.markdown("---")
+    st.caption("Teknologi: MobileNetV2 (CNN) + Long Short-Term Memory (LSTM)")

models/features.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:beffdd103295c08ce723dd1e240cb2443bdde81b72ac2123f2d0694e815a00c0
+size 679294718

models/fruit_classifier_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d8578b3c70f485e1e9287a40f03b48ed3065c419e0a7c3575d112d1cb36ab34e
+size 80666868

models/tokenizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e851bbcacf8e207484284a2b7cd83df920d3e06ea203ead6c2ecc1d7f7052af
+size 11819

requirements.txt

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+tensorflow
+numpy
+pillow
+scikit-learn
+pickle-mixin
+gradio