import streamlit as st
import cv2
from ultralytics import YOLO
from paddleocr import PaddleOCR
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array
import numpy as np
import re
from datetime import datetime
import pandas as pd
# Load YOLO models
brand_model = YOLO('b.pt') # Replace 'b.pt' with the correct path to your YOLO model for brands
ocr = PaddleOCR(lang='en') # Initialize PaddleOCR
fruit_model = load_model('DenseNet20_model.h5') # Replace with the correct path to your fruit freshness model
object_model = YOLO('yolov5s.pt') # Add object detection model
# Class names for freshness detection
class_names = {
0: 'Banana_Bad', 1: 'Banana_Good', 2: 'Fresh', 3: 'FreshCarrot', 4: 'FreshCucumber',
5: 'FreshMango', 6: 'FreshTomato', 7: 'Guava_Bad', 8: 'Guava_Good', 9: 'Lime_Bad',
10: 'Lime_Good', 11: 'Rotten', 12: 'RottenCarrot', 13: 'RottenCucumber',
14: 'RottenMango', 15: 'RottenTomato', 16: 'freshBread', 17: 'rottenBread'
}
# Custom CSS for styling
st.markdown("""
<style>
.main {
background-color: #f0f2f6;
padding: 20px;
border-radius: 10px;
}
.stButton>button {
background-color: #4CAF50;
color: white;
border-radius: 8px;
padding: 10px 24px;
font-size: 16px;
margin: 10px 0;
}
.stButton>button:hover {
background-color: #45a049;
}
.stSidebar {
background-color: #2c3e50;
color: white;
}
.stSidebar .stSelectbox {
color: black;
}
h1, h2, h3, h4, h5, h6 {
color: #2c3e50;
}
</style>
""", unsafe_allow_html=True)
# Helper function: Extract expiry dates
def extract_expiry_dates(text):
patterns = [
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # 20/07/2024 or 20-07-2024
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})', # 20/07/24 or 20-07-24
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})', # 20 MAY 2024
r'([A-Za-z]{3,}\s*\d{1,2}[,\s]*\d{4})', # July 20, 2024
r'(\d{4}[\/\-]\d{1,2}[\/\-]\d{1,2})', # 2024/07/20 or 2024-07-20
r'([A-Za-z]{3}[\-]\d{1,2}[\-]\d{4})',
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Expiry Date: 20/07/2O24
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-]\d{4}))', # Expiry Date: 20/07/2024
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Expiry Date: 20/07/2O24
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Expiry Date: 20 MAY 2O24
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*\d{4}))', # Expiry Date: 20 MAY 2024
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Expiry Date: 20 MAY 2O24
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Expiry Date: 2024/07/2O24
r'(?:exp(?:iry)?\.?\s*date\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-]\d{2}))', # Expiry Date: 2024/07/20
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{4}))', # Best Before: 2025
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Best Before: 20/07/2O24
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-]\d{4}))', # Best Before: 20/07/2024
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Best Before: 20/07/2O24
r'(?:best\s*before\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Best Before: 20 MAY 2O24
r'(?:best\s*before\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*\d{4}))', # Best Before: 20 MAY 2024
r'(?:best\s*before\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Best Before: 20 MAY 2O24
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Best Before: 2024/07/2O24
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-]\d{2}))', # Best Before: 2024/07/20
r'(?:best\s*before\s*[:\-]?\s*.*?(\d{1,2}\d{2}\d{2}))',
r'(?:best\s*before\s*[:\-]?\s*(\d{6}))',
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{1,2}[A-Za-z]{3,}[0O]\d{2}))', # Consume Before: 3ODEC2O24
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{1,2}[A-Za-z]{3,}\d{2}))', # Consume Before: 30DEC23
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Consume Before: 20/07/2O24
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-]\d{4}))', # Consume Before: 20/07/2024
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Consume Before: 20/07/2O24
r'(?:consume\s*before\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Consume Before: 20 MAY 2O24
r'(?:consume\s*before\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*\d{4}))', # Consume Before: 20 MAY 2024
r'(?:consume\s*before\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Consume Before: 20 MAY 2O24
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Consume Before: 2024/07/2O24
r'(?:consume\s*before\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-]\d{2}))', # Consume Before: 2024/07/20
r'(?:exp\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Exp: 20/07/2O24
r'(?:exp\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-]\d{4}))', # Exp: 20/07/2024
r'(?:exp\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Exp: 20/07/2O24
r'(?:exp\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Exp: 20 MAY 2O24
r'(?:exp\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*\d{4}))', # Exp: 20 MAY 2024
r'(?:exp\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Exp: 20 MAY 2O24
r'(?:exp\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Exp: 2024/07/2O24
r'(?:exp\s*[:\-]?\s*.*?(\d{4}[\/\-]\d{2}[\/\-]\d{2}))', # Exp: 2024/07/20
r'Exp\.Date\s+(\d{2}[A-Z]{3}\d{4})',
r'(?:exp\s*\.?\s*date\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Exp. Date: 16 MAR 2O30 (with typo)
r'(?:exp\s*\.?\s*date\s*[:\-]?\s*.*?(\d{2}[\/\-]\d{2}[\/\-][0O]\d{2}))', # Exp. Date: 15/12/2O30 (with typo)
r'(?:exp\s*\.?\s*date\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Exp. Date: 15 MAR 2O30 (with typo)
r'(?:exp\s*\.?\s*date\s*[:\-]?\s*.?(\d{2}\s[A-Za-z]{3,}\s*[0O]\d{2}))', # Exp. Date cdsyubfuyef 15 MAR 2O30 (with typo)
r'(\d{2}[\/\-]\d{2}[\/\-]\d{4})', # 20/07/2024
r'(\d{2}[\/\-]\d{2}[\/\-]\d{2})', # 20/07/24
r'(\d{2}\s*[A-Za-z]{3,}\s*\d{4})', # 20 MAY 2024
r'(\d{2}\s*[A-Za-z]{3,}\s*\d{2})', # 20 MAY 24
r'(\d{4}[\/\-]\d{2}[\/\-]\d{2})', # 2024/07/20
r'(\d{4}[\/\-]\d{2}[\/\-]\d{2})', # 2024-07-20
r'(\d{4}[A-Za-z]{3,}\d{2})', # 2024MAY20
r'(\d{2}[A-Za-z]{3,}\d{4})', # 20MAY2024
r'(?:exp\.?\s*date\s*[:\-]?\s*(\d{2}\s*[A-Za-z]{3,}\s*(\d{4}|\d{2})))',
r'(?:exp\.?\s*date\s*[:\-]?\s*(\d{2}\s*\d{2}\s*\d{4}))', # Exp. Date: 20 05 2025
r'(\d{4}[A-Za-z]{3}\d{2})', # 2025MAY11
r'(?:best\s*before\s*[:\-]?\s*(\d+)\s*(days?|months?|years?))', # Best Before: 6 months
r'(?:best\s*before\s*[:\-]?\s*(three)\s*(months?))',
r'(\b(?:Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)\b\s*\d{4})',
r'\bUSE BY\s+(\d{1,2}[A-Za-z]{3}\d{4})\b',
r'Exp\.Date\s*(\d{2}[A-Z]{3}\d{4})',
r'EXP:\d{4}/\d{2}/\d{4}/\d{1}/[A-Z]', # JAN-15-2024
r'USE BY[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Use by date
r'BEST BEFORE[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Best before date
r'EXPIRY DATE[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expiry Date
r'EXPIRY[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expiry
r'EXP[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Exp
r'VALID UNTIL[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Valid Until
r'CONSUME BY[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Consume By
r'EXPIRES ON[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expires On
# DDMMMYYYY format
r'(\d{1,2}[A-Za-z]{3}\d{4})', # DDMMMYYYY format
# Short year formats
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})', # Short year date format (DD/MM/YY)
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # General date format (DD/MM/YYYY)
# Year-month-day formats
r'(\d{4}[\/\-]\d{1,2}[\/\-]\d{1,2})', # Year-month-day format (YYYY/MM/DD)
# Month/Year formats
r'(\d{1,2}[\/\-]\d{1,2})', # MM/DD format
r'(\d{1,2}[\/\-]\d{2})', # MM/YY format
# Month name formats
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})', # Month name with day and year
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{2})', # Month name with day and short year
# Year with month name
r'(\d{4}[A-Za-z]{3,}\d{1,2})', # Year with month name
r'(\d{1,2}[A-Za-z]{3,}\d{4})', # Day with month name and full year
# Additional formats
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})', # MM/DD/YY format
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # MM/DD/YYYY format
r'(\d{1,2}[\/\-]\d{1,2})', # MM/DD format
r'(\d{1,2}[\/\-]\d{2})', # MM/YY format
# Best before phrases
r'Best before (\d+) months', # Best before in months
r'Expiration Date[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expiration Date
r'Expires[:\-]?\s*(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expires
# Additional variations
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})', # Month name with day and year
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{2})', # Month name with day and short year
# More variations
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # MM/DD/YYYY format
r'(\d{1,2}[\/\-]\d{1,2})', # MM/DD format
r'(\d{1,2}[\/\-]\d{2})', # MM/YY format
# Additional expiry phrases
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expiry in various formats
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})', # Expiry in short year formats
r'(\d{1,2}[\/\-]\d{1,2})', # Expiry in MM/DD format
r'(\d{1,2}[\/\-]\d{2})', # Expiry in MM/YY format
# Additional phrases
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})', # Month name with day and year
r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{2})', # Month name with day and short year
r'(\d{4}[A-Za-z]{3,}\d{1,2})', # Year with month name
r'(\d{1,2}[A-Za-z]{3,}\d{4})', # Day with month name and full year
# Additional expiry phrases
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})', # Expiry in various formats
r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})', # Expiry in short year formats
r'(\d{1,2}[\/\-]\d{1,2})', # Expiry in MM/DD format
r'(\d{1,2}[\/\-]\d{2})', # Expiry in MM/YY format
]
dates = []
for pattern in patterns:
matches = re.findall(pattern, text)
dates.extend(matches)
unique_dates = sorted(dates, key=lambda x: len(x), reverse=True)
return [unique_dates[0]] if unique_dates else [] # Return the most likely date
# Helper function: Preprocess image for fruit freshness
def preprocess_image(image):
img = cv2.resize(image, (128, 128))
img_array = img_to_array(img)
img_array = np.expand_dims(img_array, axis=0)
img_array = img_array / 255.0
return img_array
# Helper function: Calculate days to expiry
def calculate_days_to_expiry(expiry_dates):
results = []
today = datetime.now()
for date_str in expiry_dates:
try:
if '/' in date_str or '-' in date_str:
if len(date_str.split('/')[-1]) == 2 or len(date_str.split('-')[-1]) == 2:
date_obj = datetime.strptime(date_str, '%d/%m/%y') if '/' in date_str else datetime.strptime(date_str, '%d-%m-%y')
else:
date_obj = datetime.strptime(date_str, '%d/%m/%Y') if '/' in date_str else datetime.strptime(date_str, '%d-%m-%Y')
else:
date_obj = datetime.strptime(date_str, '%d %B %Y')
delta = (date_obj - today).days
if delta >= 0:
results.append(f"{date_str}: {delta} days to expire")
else:
results.append(f"{date_str}: Expired")
except ValueError:
results.append(f"{date_str}: Invalid date format")
return results
# Initialize session state
if 'uploaded_file' not in st.session_state:
st.session_state['uploaded_file'] = None
# Streamlit App
st.title("Flipkart Grid")
# Sidebar options
app_mode = st.sidebar.selectbox(
"Choose the mode",
["Home", "Brand & Text Detection", "Fruit Freshness Detection", "Object Detection"],
on_change=lambda: st.session_state.update({'uploaded_file': None}) # Reset uploaded file on mode change
)
if app_mode == "Home":
st.markdown("""
## Welcome to the Detection App
Use the sidebar to choose between:
- *Brand & Text Detection*: Detect brands, extract text, and identify expiry dates from uploaded images.
- *Fruit Freshness Detection*: Detect and classify the freshness of fruits from uploaded images.
- *Object Detection*: Detect objects in uploaded images.
""")
else:
st.header(app_mode)
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"], key=app_mode)
if uploaded_file is not None:
st.session_state['uploaded_file'] = uploaded_file
if st.session_state['uploaded_file'] is not None:
file_bytes = np.asarray(bytearray(st.session_state['uploaded_file'].read()), dtype=np.uint8)
image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR)
if app_mode == "Brand & Text Detection":
# Brand detection
results = brand_model.predict(source=image, stream=False)
detected_image = results[0].plot()
# OCR for text extraction
_, img_buffer = cv2.imencode('.jpg', image)
ocr_result = ocr.ocr(img_buffer.tobytes())
if ocr_result and isinstance(ocr_result[0], list) and len(ocr_result[0]) > 0:
extracted_text = ' '.join([line[1][0] for line in ocr_result[0]])
expiry_dates = extract_expiry_dates(extracted_text)
expiry_info = calculate_days_to_expiry(expiry_dates)
else:
extracted_text = "No text detected"
expiry_dates = []
expiry_info = []
# Count objects
object_counts = {}
for box in results[0].boxes.data.cpu().numpy():
label = results[0].names[int(box[5])]
object_counts[label] = object_counts.get(label, 0) + 1
# Display results
st.image(cv2.cvtColor(detected_image, cv2.COLOR_BGR2RGB), caption="Detected Image")
st.markdown(f"*Extracted Text:* {extracted_text}")
st.markdown(f"*Expiry Dates:*")
if expiry_info:
for info in expiry_info:
st.markdown(f"- {info}")
else:
st.markdown("None")
st.markdown("*Object Counts:*")
for label, count in object_counts.items():
st.markdown(f"- {label}: {count}")
elif app_mode == "Fruit Freshness Detection":
# Preprocess and predict
img_array = preprocess_image(image)
predictions = fruit_model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)[0]
label = class_names[predicted_class]
confidence = predictions[0][predicted_class] * 100
# Display results
st.image(cv2.cvtColor(image, cv2.COLOR_BGR2RGB), caption="Uploaded Image")
st.markdown(f"*Label:* {label}")
st.markdown(f"*Confidence:* {confidence:.2f}%")
elif app_mode == "Object Detection":
# Object Detection
results = object_model.predict(source=image, stream=False)
detected_objects = []
for result in results:
boxes = result.boxes.data.cpu().numpy()
for box in boxes:
class_id = int(box[5])
confidence = box[4] # Assuming the confidence score is at index 4
detected_objects.append((result.names[class_id], confidence))
# Draw bounding box and label on the image
x1, y1, x2, y2 = map(int, box[:4])
label = f"{result.names[class_id]} {confidence * 100:.2f}%"
cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2)
cv2.putText(image, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
# Convert the image back to RGB for display in Streamlit
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
st.image(image_rgb, caption='Detected Objects', use_container_width=True)
# Count occurrences and average confidence of each object
object_data = {}
for obj, confidence in detected_objects:
if obj in object_data:
object_data[obj]['count'] += 1
object_data[obj]['total_confidence'] += confidence
else:
object_data[obj] = {'count': 1, 'total_confidence': confidence}
# Prepare data for display
object_display_data = [
{'Object': obj, 'Count': data['count'], 'Average Confidence': data['total_confidence'] / data['count']}
for obj, data in object_data.items()
]
# Display detected objects in a table with column names
st.write("Detected Objects and Counts:")
st.table(pd.DataFrame(object_display_data))