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.gitignore

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+flagged/
+*.pt
+*.png
+*.mp4
+*.mkv
+gradio_cached_examples/

README.md

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 ---
-title: Test
-emoji: 🔥
-colorFrom: pink
-colorTo: pink
+title: Low Light Image Recognition
+emoji: 🐢
+colorFrom: purple
+colorTo: green
 sdk: gradio
-sdk_version: 3.45.2
+sdk_version: 3.40.1
 app_file: app.py
 pinned: false
 license: mit

app.py

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+import gradio as gr
+import cv2
+import requests
+import gdown
+import tensorflow as tf
+from tensorflow import keras
+#from custom_model import ImageClassifier
+import numpy as np
+#from tensorflow.keras import models
+#from keras.layers import Dense, Activation, Flatten,Dropout, Conv2D, BatchNormalization, MaxPooling2D
+
+from keras.models import load_model
+
+
+
+ 
+
+path  = [['car_bike.jpg'], ['human.jpg'], ['chair.jpg']]
+
+        
+url = 'https://drive.google.com/file/d/1PCb6MTqelw7Tk0iCxo5Ef70zYwEpsI4Y/view?usp=sharing'
+output_path = 'classlabel.txt'
+gdown.download(url, output_path, quiet=False,fuzzy=True)
+
+with open(output_path,'r') as file:
+    LABELS = [x.strip() for x in file.readlines()]
+
+
+num_classes = 12
+IMG_SIZE = 124
+
+def _normalize_img(img):
+    img = tf.cast(img, tf.float32)/255.   # All images will be rescaled by 1./255
+    img = tf.image.resize(img,  (IMG_SIZE, IMG_SIZE), method= 'bilinear')
+    return (img)
+
+
+
+model = load_model("model.h5")
+
+
+def predict_fn(img):
+     img = img.convert('RGB')
+     img_data = _normalize_img(img)
+     x = np.array(img_data)
+     x = np.expand_dims(x, axis=0)
+     temp = model.predict(x)
+     
+     idx = np.argsort(np.squeeze(temp))[::-1]
+     top3_value = np.asarray([temp[0][i] for i in idx[0:3]])
+     top3_idx = idx[0:3]
+     
+     return {LABELS[i]:str(v) for i,v in zip(top3_idx,top3_value)}
+     
+
+
+gr.Interface(predict_fn, gr.inputs.Image(type='pil'), outputs='label', examples=path,).launch()

classlabel.txt

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+Bicycle
+Boat
+Bottle
+Bus
+Car
+Cat
+Chair
+Cup
+Dog
+Motorbike
+People
+Table

requirements.txt

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+# Base ----------------------------------------
+matplotlib>=3.2.2
+numpy>=1.21.6
+opencv-python>=4.6.0
+Pillow>=7.1.2
+PyYAML>=5.3.1
+requests>=2.23.0
+scipy>=1.4.1
+gradio>=3.36.1
+tensorflow==2.12.0
+tensorflow-datasets==4.9.2
+
+
+# Plotting ------------------------------------
+pandas>=1.1.4
+seaborn>=0.11.0
+gdown
+
+
+# Extras --------------------------------------
+psutil  # system utilization
+thop>=0.1.1  # FLOPs computation