git init git add . git commit -m "Initial commit"

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git remote add origin https://github.com/your-username/inventory-automation.git git push -u origin master

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git add .

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import cv2 import tensorflow as tf import numpy as np

def load_model(model_path):

Load pre-trained AI model

model = tf.keras.models.load_model(model_path)
return model

def process_image(image_path):

Pre-process the image for AI model

image = cv2.imread(image_path)
image_resized = cv2.resize(image, (224, 224))
return np.expand_dims(image_resized, axis=0)

def predict_item(model, image):

Predict item in the image using the model

predictions = model.predict(image)
item = np.argmax(predictions)
return item

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def assess_damage(image):

Placeholder function to assess damage

damage_level = 0  # No damage = 0, Minor = 1, Major = 2
# Logic to detect damage (e.g., color analysis, edge detection, etc.)
return damage_level

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import requests

API_URL = 'https://api.encircleapp.com/v1/claims/{claim_id}/inventory' API_KEY = 'your_api_key_here'

Function to upload data to Encircle API

def upload_inventory_data(item_data, claim_id): headers = { 'Authorization': f'Bearer {API_KEY}', 'Content-Type': 'application/json' } response = requests.post(API_URL.format(claim_id=claim_id), json=item_data, headers=headers) if response.status_code == 201: print("Data successfully uploaded.") else: print(f"Failed to upload data. Status code: {response.status_code}") print(response.json()) return response.json()

Generate the payload to send to the API

def generate_item_payload(item, damage_level, description): payload = { 'item': item, 'damage_level': damage_level, 'description': description } return payload

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def verify_data(item, damage_level, description): print(f"Item: {item}") print(f"Damage Level: {damage_level}") print(f"Description: {description}") confirmation = input("Do you want to submit this data? (yes/no): ") return confirmation.lower() == 'yes'

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def main(image_path, model_path, claim_id):

Load the model

model = load_model(model_path)

# Process the image and make predictions
image = process_image(image_path)
item = predict_item(model, image)

# Assess the damage
damage_level = assess_damage(image)

# Generate a description (this could be generated based on your needs)
description = "Description of the item"

# Verify the data
if verify_data(item, damage_level, description):
    # Create the payload for submission
    item_data = generate_item_payload(item, damage_level, description)

    # Upload to the Encircle API
    upload_inventory_data(item_data, claim_id)
else:
    print("Submission cancelled.")

Example usage

if name == "main": image_path = "/path/to/image.jpg" model_path = "/path/to/model.h5" claim_id = "12345678" main(image_path, model_path, claim_id)

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pip install tensorflow opencv-python requests

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python main.py --image_path /path/to/image.jpg --model_path /path/to/model.h5 --claim_id 12345678

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git add .

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git remote add origin https://github.com/your-username/inventory-automation.git

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git push -u origin master

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cd path/to/your/repository

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touch object_detection.py

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from google.cloud import vision

def detect_labels(path): """Detects labels in the file.""" client = vision.ImageAnnotatorClient()

with open(path, 'rb') as image_file:
    content = image_file.read()

image = vision.Image(content=content)
response = client.label_detection(image=image)
labels = response.label_annotations

print('Labels:')
for label in labels:
    print(label.description)

Example usage (replace 'path_to_your_photo.jpg' with actual image file path)

detect_labels('path_to_your_photo.jpg')

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pip install google-cloud-vision

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export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/key.json"

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touch requirements.txt

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google-cloud-vision

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detect_labels('')

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detect_labels('test_image.jpg')

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python object_detection.py

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git add object_detection.py requirements.txt git commit -m "Added Google Vision API for object detection"

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git push origin main

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key.json

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Google Vision API Object Detection

This script uses the Google Vision API to detect objects in images and print labels.

Setup

1. Clone the repository.
2. Install dependencies:

   
   pip install -r requirements.txt

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export GOOGLE_APPLICATION_CREDENTIALS="path/to/your/key.json"

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python object_detection.py