Delete Nutri-Label

Nutri-Label/app.py

@@ -1,267 +0,0 @@
-import streamlit as st
-import cv2
-import numpy as np
-import re
-import os
-import pandas as pd
-from PIL import Image
-import time
-from ultralytics import YOLO
-from paddleocr import PaddleOCR, draw_ocr
-
-st.title("Nutri-Grade Label Detection & Grade Calculator")
-
-# -----------------------------------------------
-# Info & Petunjuk Penggunaan
-# -----------------------------------------------
-with st.expander("Info & Petunjuk Penggunaan"):
-    st.markdown("""
-    **Deskripsi Aplikasi:**
-
-    Aplikasi ini membantu Anda mendeteksi dan mengekstrak informasi tabel gizi dari gambar label nutrisi, melakukan normalisasi nilai nutrisi per 100 g/ml, dan menghitung Nutri-Grade sesuai dengan standar resmi (Rev. Juni 2023).
-
-    **Fitur Utama:**
-    - Deteksi objek label nutrisi dengan YOLO.
-    - Ekstraksi teks dengan PaddleOCR, mendukung format "key: value".
-    - Normalisasi nilai nutrisi (Gula dan Lemak Jenuh) per 100 g/ml.
-    - Perhitungan grade berdasarkan threshold:
-         • Gula: Grade A ≤ 1g, B: >1-5g, C: >5-10g, D: >10g per 100 ml.
-         • Lemak Jenuh: Grade A ≤ 0.7g, B: >0.7-1.2g, C: >1.2-2.8g, D: >2.8g per 100 ml.
-         • **Grade akhir diambil dari nilai terburuk antara gula dan lemak jenuh.**
-
-    **Cara Penggunaan:**
-    1. Upload gambar label nutrisi (JPG/PNG).
-    2. Sistem mendeteksi objek dan mengekstrak nilai nutrisi.
-    3. Periksa dan koreksi nilai secara manual jika diperlukan.
-    4. Klik *Hitung* untuk melihat tabel normalisasi dan grade.
-    """)
-
-with st.expander("!! Tolong Diperhatikan !!"):
-    st.markdown("""
-    Labelisasi di bawah hanya sebagai gambaran umum. Perlu riset lebih lanjut untuk akurasi.
-
-    **Pengembangan:**
-    - Konsultasi dengan nutritionist untuk parameter yang lebih tepat.
-    - Integrasi informasi halal, kalori, dan fitur interaktif (misal: chatbot).
-    """)
-
-# Fungsi untuk membersihkan nilai numerik (contoh: "15g" → 15.0)
-def parse_numeric_value(text):
-    cleaned = re.sub(r"[^\d\.\-]", "", text)
-    try:
-        return float(cleaned)
-    except ValueError:
-        return 0.0
-
-# Inisialisasi model YOLO dan PaddleOCR
-trained_model_path = "best.pt"  # Pastikan file model YOLO ada di working directory
-yolo_model = YOLO(trained_model_path)
-ocr_model = PaddleOCR(use_gpu=True, lang='en', cls=True)
-
-# --- STEP 1: Upload Gambar ---
-uploaded_file = st.file_uploader("Upload Gambar (JPG/PNG)", type=["jpg", "jpeg", "png"])
-if uploaded_file is not None:
-    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
-    img = cv2.imdecode(file_bytes, 1)
-    st.image(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), caption="Gambar yang diupload", use_column_width=True)
-    img_path = "uploaded_image.jpg"
-    cv2.imwrite(img_path, img)
-
-    # --- STEP 2: Object Detection & Crop dengan YOLO ---
-    st.write("Melakukan object detection dengan YOLO dan crop region...")
-    yolo_results = yolo_model.predict(source=img_path, conf=0.5)
-    crop_images = []
-    boxes = yolo_results[0].boxes
-    for i, box in enumerate(boxes):
-        x1, y1, x2, y2 = box.xyxy[0].cpu().numpy().astype(int)
-        cropped = img[y1:y2, x1:x2]
-        crop_filename = f"crop_{i}.jpg"
-        cv2.imwrite(crop_filename, cropped)
-        crop_images.append((crop_filename, cropped))
-    st.success("Proses crop bounding box selesai!")
-    st.write("Jumlah crop yang ditemukan:", len(crop_images))
-    for crop_filename, cropped in crop_images:
-        st.image(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB), caption=f"Crop: {crop_filename}", use_column_width=True)
-
-    # --- STEP 3: OCR pada Gambar Penuh ---
-    st.write("Melakukan OCR pada gambar penuh dengan PaddleOCR...")
-    start_time = time.time()
-    ocr_result = ocr_model.ocr(img_path, cls=True)
-    ocr_time = time.time() - start_time
-    st.write(f"Waktu pemrosesan OCR: {ocr_time:.2f} detik")
-
-    if not ocr_result or len(ocr_result[0]) == 0:
-        st.error("OCR tidak menemukan teks pada gambar!")
-    else:
-        # Ekstrak data OCR
-        ocr_data = ocr_result[0]
-        ocr_list = []
-        for line in ocr_data:
-            box = line[0]
-            text = line[1][0]
-            score = line[1][1]
-            xs = [pt[0] for pt in box]
-            ys = [pt[1] for pt in box]
-            center_x = sum(xs) / len(xs)
-            center_y = sum(ys) / len(ys)
-            ocr_list.append({
-                "text": text,
-                "box": box,
-                "score": score,
-                "center_x": center_x,
-                "center_y": center_y,
-                "height": max(ys) - min(ys)
-            })
-        # Urutkan berdasarkan posisi vertikal
-        ocr_list = sorted(ocr_list, key=lambda x: x["center_y"])
-
-        # Ekstrak pasangan key-value dengan format "key: value"
-        # Hanya ekstrak gula, takaran saji, dan lemak jenuh
-        target_keys = {
-            "gula": ["gula"],
-            "takaran saji": ["takaran saji", "serving size"],
-            "lemak jenuh": ["lemak jenuh"]
-        }
-        extracted = {}
-        # Pass 1: Ekstraksi menggunakan tanda titik dua
-        for item in ocr_list:
-            txt_lower = item["text"].lower()
-            if ":" in txt_lower:
-                parts = txt_lower.split(":")
-                key_candidate = parts[0].strip()
-                value_candidate = parts[-1].strip()
-                for canonical, variants in target_keys.items():
-                    for variant in variants:
-                        if variant in key_candidate and canonical not in extracted:
-                            clean_value = re.sub(r"[^\d\.\-]", "", value_candidate)
-                            if clean_value and clean_value != ".":
-                                extracted[canonical.capitalize()] = clean_value
-                            break
-        # Pass 2: Fallback untuk key yang belum diekstrak
-        for item in ocr_list:
-            txt_lower = item["text"].lower()
-            for canonical, variants in target_keys.items():
-                if canonical not in extracted:
-                    for variant in variants:
-                        if variant in txt_lower:
-                            key_center = (item["center_x"], item["center_y"])
-                            key_height = item["height"]
-                            best_candidate = None
-                            min_dx = float('inf')
-                            for other in ocr_list:
-                                if other == item:
-                                    continue
-                                if other["center_x"] > key_center[0] and abs(other["center_y"] - key_center[1]) < 0.5 * key_height:
-                                    dx = other["center_x"] - key_center[0]
-                                    if dx < min_dx:
-                                        min_dx = dx
-                                        best_candidate = other
-                            if best_candidate:
-                                raw_value = best_candidate["text"]
-                                clean_value = re.sub(r"[^\d\.\-]", "", raw_value)
-                                if clean_value and clean_value != ".":
-                                    extracted[canonical.capitalize()] = clean_value
-                            break
-
-        if extracted:
-            st.write("**Hasil Ekstraksi Key-Value:**")
-            for k, v in extracted.items():
-                st.write(f"{k}: {v}")
-        else:
-            st.warning("Tidak ditemukan pasangan key-value yang cocok.")
-
-        # Tampilkan hasil OCR dengan bounding box untuk referensi
-        boxes_ocr = [line["box"] for line in ocr_list]
-        texts_ocr = [line["text"] for line in ocr_list]
-        scores_ocr = [line["score"] for line in ocr_list]
-        im_show = draw_ocr(Image.open(img_path).convert("RGB"), boxes_ocr, texts_ocr, scores_ocr, font_path="simfang.ttf")
-        im_show = Image.fromarray(im_show)
-        st.image(im_show, caption="Hasil OCR dengan Bounding Boxes", use_column_width=True)
-
-    # --- Koreksi Manual dengan st.form ---
-    with st.form("correction_form"):
-        st.write("Silakan koreksi nilai jika diperlukan (hanya angka, tanpa satuan):")
-        corrected_data = {}
-        for key in target_keys.keys():
-            key_cap = key.capitalize()
-            current_val = str(parse_numeric_value(extracted.get(key_cap, ""))) if key_cap in extracted else ""
-            new_val = st.text_input(f"{key_cap}", value=current_val)
-            corrected_data[key_cap] = new_val
-        submit_button = st.form_submit_button("Hitung")
-
-    if submit_button:
-        try:
-            serving_size = parse_numeric_value(corrected_data.get("Takaran saji", "100"))
-        except:
-            serving_size = 0.0
-
-        # Ambil nilai nutrisi (hanya gula dan lemak jenuh)
-        sugar_value = parse_numeric_value(corrected_data.get("Gula", "0"))
-        fat_value   = parse_numeric_value(corrected_data.get("Lemak jenuh", "0"))
-
-        if serving_size > 0:
-            sugar_norm = (sugar_value / serving_size) * 100
-            fat_norm   = (fat_value / serving_size) * 100
-        else:
-            st.error("Takaran saji tidak valid untuk normalisasi.")
-            sugar_norm, fat_norm = sugar_value, fat_value
-
-        st.write("**Tabel Hasil Normalisasi per 100 g/ml**")
-        data_tabel = {
-            "Nutrisi": ["Gula", "Lemak jenuh"],
-            "Nilai (per 100 g/ml)": [sugar_norm, fat_norm]
-        }
-        df_tabel = pd.DataFrame(data_tabel)
-        st.table(df_tabel)
-
-        # Fungsi untuk menghitung grade berdasarkan threshold
-        def grade_from_value(value, thresholds):
-            if value <= thresholds["A"]:
-                return "Grade A"
-            elif value <= thresholds["B"]:
-                return "Grade B"
-            elif value <= thresholds["C"]:
-                return "Grade C"
-            else:
-                return "Grade D"
-
-        # Threshold sesuai panduan Nutri-Grade (g/100ml)
-        thresholds_sugar = {"A": 1.0, "B": 5.0, "C": 10.0}
-        thresholds_fat   = {"A": 0.7, "B": 1.2, "C": 2.8}
-
-        sugar_grade = grade_from_value(sugar_norm, thresholds_sugar)
-        fat_grade   = grade_from_value(fat_norm, thresholds_fat)
-
-        # Grade akhir diambil dari nilai terburuk (nilai maksimum skor)
-        grade_scores = {"Grade A": 1, "Grade B": 2, "Grade C": 3, "Grade D": 4}
-        worst_score = max(grade_scores[sugar_grade], grade_scores[fat_grade])
-        inverse_scores = {v: k for k, v in grade_scores.items()}
-        final_grade = inverse_scores[worst_score]
-
-        st.write(f"**Grade Gula:** {sugar_grade}")
-        st.write(f"**Grade Lemak Jenuh:** {fat_grade}")
-        st.write(f"**Grade Akhir:** {final_grade}")
-
-        def color_grade(grade_text):
-            if grade_text == "Grade A":
-                bg_color = "#2ecc71"
-            elif grade_text == "Grade B":
-                bg_color = "#f1c40f"
-            elif grade_text == "Grade C":
-                bg_color = "#e67e22"
-            else:
-                bg_color = "#e74c3c"
-            return f"""
-            <div style="
-                background-color: {bg_color};
-                padding: 10px;
-                border-radius: 5px;
-                margin-top: 10px;
-                font-weight: bold;
-                color: white;
-                text-align: center;
-            ">
-            {grade_text}
-            </div>
-            """
-        st.markdown(color_grade(final_grade), unsafe_allow_html=True)

Nutri-Label/best.pt

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9ffd3c6552428cee4cd5895053bd1ea6b1f4e7815d46bd03807c243ac75d17ca
-size 6248483

Nutri-Label/requrements.txt

@@ -1,9 +0,0 @@
---extra-index-url=https://www.paddlepaddle.org.cn/packages/stable/cpu/
-paddlepaddle==3.0.0rc1
-paddleocr
-ultralytics
-streamlit
-numpy
-opencv-python
-pandas
-Pillow

Nutri-Label/simfang.ttf

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:521c6f7546b4eb64fa4b0cd604bbd36333a20a57e388c8e2ad2ad07b9e593864
-size 10576012