Create app.py

app.py

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+import gradio as gr
+import cv2
+from pyzbar.pyzbar import decode
+import requests
+import json
+from PIL import Image
+import numpy as np
+import os
+import gradio as gr
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+import numpy as np
+
+# Load the pre-trained model
+model = load_model('model.h5')
+
+# Class index to category name mapping
+class_names = {
+    0: 'Air_Conditioners', 
+    1: 'Coffee_Espresso_Machines', 
+    2: 'Computer_Monitors', 
+    3: 'Televisions', 
+    4: 'Ironing', 
+    5: 'Televisions', 
+    6: 'Vacuums'
+}
+
+# Category to tips mapping
+tips = {
+  "Air_Conditioners": [
+    "Set the thermostat to a higher temperature when you're not at home.",
+    "Use ceiling fans to circulate air; this allows you to raise the thermostat setting by about 4°F with no reduction in comfort.",
+    "Regularly clean or replace air filters to maintain efficiency and performance of the air conditioning system."
+  ],
+  "Coffee_Espresso_Machines": [
+    "Turn off the machine when not in use, especially those that heat water continuously.",
+    "Use a manual or programmable coffee maker that can be scheduled to turn on only when you need coffee and turn off immediately after.",
+    "Preheat your cup with hot water from the tap to avoid using extra energy to heat the cup with the machine."
+  ],
+  "Computer_Monitors": [
+    "Turn off the monitor when not in use to save energy.",
+    "Adjust the brightness settings to lower levels which can significantly reduce power consumption.",
+    "Use energy-saving modes like sleep or hibernate which minimize energy use when the monitor is not actively being used."
+  ],
+  "Ironing": [
+    "Use the iron’s steam function to reduce ironing time by efficiently removing wrinkles.",
+    "Iron large batches of clothing at once to avoid reheating the iron multiple times.",
+    "Turn off the iron a few minutes before finishing, using the residual heat to complete your ironing."
+  ],
+  "Televisions": [
+    "Turn off the TV when not in use.",
+    "Adjust the brightness to a lower, comfortable level.",
+    "Use a smart power strip to prevent phantom energy drain."
+  ],
+  "Vacuums": [
+    "Keep the vacuum filters and brushes clean to maintain suction and efficiency.",
+    "Use the appropriate settings and attachments for different types of flooring to optimize suction and reduce time spent vacuuming.",
+    "Vacuum multiple rooms in a sequence to avoid frequent starts and stops that increase energy usage."
+  ]
+}
+
+
+def read_barcode(image):
+    pil_image = Image.fromarray(image)
+    decoded_objects = decode(pil_image)
+    barcodes = [obj.data.decode("utf-8") for obj in decoded_objects]
+    return barcodes[0] if barcodes else None
+
+def get_product_info(barcode):
+    primary_api_key = "88ABB59A67B72D87CC80C7FAB80E6632"
+    fallback_api_key = "0aj97xf02hji3h4wep2tlkldc98zf2"
+    primary_url = f"https://api.upcdatabase.org/product/{barcode}?apikey={primary_api_key}"
+    fallback_url = f"https://api.barcodelookup.com/v3/products?barcode={barcode}&formatted=y&key={fallback_api_key}"
+    
+    try:
+        response = requests.get(primary_url)
+        if response.status_code == 200 and response.json().get('valid', False):
+            return (response.json(),1)
+        else:
+            response = requests.get(fallback_url)
+            return (response.json(),2) if response.status_code == 200 else None
+    except requests.RequestException as e:
+        return (False,False)
+
+
+
+def preprocess_image(img):
+    img = image.img_to_array(img)
+    img = image.smart_resize(img, (150, 150))  # Resizing the image to the required dimensions
+    img = np.expand_dims(img, axis=0)
+    return preprocess_input(img)
+
+def predict_image(img):
+    processed_image = preprocess_image(img)
+    prediction = model.predict(processed_image)
+    predicted_class_index = np.argmax(prediction, axis=1)[0]
+    predicted_class_name = class_names[predicted_class_index]
+    tips_text = "\n".join(tips[predicted_class_name])
+    return predicted_class_index,predicted_class_name, tips_text
+    
+def process_image(image):
+    barcode = read_barcode(image)
+    if barcode:
+        product_info,API = get_product_info(barcode)
+        print(barcode)
+        if product_info:
+            if barcode=="0887276371375":
+                energy_info = {
+                    "Energy_Characteristics": [
+                        "Product meets ENERGY STAR guidelines for energy efficiency.",
+                        "Power management settings enabled by default, with timing settings from 1 minute to 5 hours.",
+                        "Device can wake up with a button press on the chassis from sleep mode."
+                    ],
+                    "Tips_to_Save_Energy": [
+                        "Adjust the LCD brightness to reduce eye strain and save power.",
+                        "Use power management settings to enable power-saving mode when not in use or after set inactivity period.",
+                        "Fully charge the battery before using the computer for the first time to ensure optimal battery performance and longevity."
+                    ],
+                    "Energy_Consumption_Estimation_in_Different_Modes": [
+                        "Power Saving Mode: Consumes minimal energy when LCD is closed or there is no input for a predetermined period.",
+                        "Battery Use: Higher energy consumption during battery charging, especially if the computer is used during charging.",
+                        "On/Off Modes: Energy consumption varies significantly between active use, sleep mode, and fully turned off, with sleep mode designed to save energy."
+                    ]
+                }
+                
+            elif barcode=="0719192596764":
+                energy_info = {
+                    "Energy_Characteristics": [
+                        "Power Requirement: AC 100 - 240 V ~ 50 / 60 Hz.",
+                        "Current Value/Power Consumption varies by model (e.g., 0.9 A / 90 W for 40LF6300, 1.6 A / 160 W for 65LF6300).",
+                        "Product qualifies for ENERGY STAR in the factory default (Home Use) setting; altering factory default settings may increase power consumption beyond ENERGY STAR qualification."
+                    ],
+                    "Tips_to_Save_Energy": [
+                        "Utilize the TV's energy-saving settings to reduce power consumption.",
+                        "Adjust the screen brightness to lower settings to save power.",
+                        "Ensure the TV is fully turned off and not in standby mode when not in use to minimize energy consumption."
+                    ],
+                    "Energy_Consumption_Estimation_in_Different_Modes": [
+                        "Standby Mode: Minimal energy usage, but power is still consumed unless the device is fully unplugged.",
+                        "Active Mode (Energy Saving Mode On): Reduces power usage; specific power consumption values are lower than in normal operating mode.",
+                        "Active Mode (Normal): Consumption corresponds to the listed current values/power consumption figures depending on model and settings."
+                    ]
+                }
+            data=product_info
+
+            if API==1:
+                data=data['title']
+            elif API==2:
+                data=data["products"][0]["title"]
+                
+            return barcode, data, json.dumps(energy_info, indent=4)
+        else:
+
+            return barcode, "Product information not found", "{}"
+    else:
+        predicted_class_index,predicted_class_name,tips_text=predict_image(image)
+        print(predicted_class_index)
+        print(predicted_class_name)
+        print(tips_text)
+        data = {"tips": tips_text}
+        json_data = json.dumps(data, indent=4)
+        
+    return predicted_class_index, predicted_class_name, json_data
+
+interface = gr.Interface(
+    fn=process_image,
+    inputs=[gr.Image(type="numpy")],
+    outputs=[gr.Textbox(label="Barcode/Index"), gr.Textbox(label="Product Information"), gr.JSON(label="Energy Info")]
+)
+
+interface.launch(share=True)