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import gradio as gr
import json
import requests
from PIL import Image
import os
# --- Constants for the API ---
API_URL = "https://predict.ultralytics.com"
# It's recommended to use Hugging Face secrets for API keys
API_KEY = os.environ.get("ULTRALYTICS_API_KEY")
MODEL_ID = "https://hub.ultralytics.com/models/RsLHnWMhiBPqy3iFZAgr"
def classify_image(image):
"""
Takes an image, sends it to the Ultralytics API, and returns the
classification name and confidence score.
"""
# Convert the Gradio image (numpy array) to a file-like object
image_pil = Image.fromarray(image)
image_path = "temp_image.jpg"
image_pil.save(image_path)
headers = {"x-api-key": API_KEY}
data = {
"model": MODEL_ID,
"imgsz": 640,
"conf": 0.25,
"iou": 0.45
}
try:
with open(image_path, "rb") as f:
response = requests.post(API_URL, headers=headers, data=data, files={"file": f})
# Check for a successful response
response.raise_for_status()
# Parse the JSON response
result_data = response.json()
# Extract the relevant information
# The structure is data -> images -> [0] -> results -> [0]
if result_data.get("images") and result_data["images"][0].get("results"):
top_result = result_data["images"][0]["results"][0]
name = top_result.get("name")
confidence = top_result.get("confidence")
# Return the extracted values for the two output components
return name, f"{confidence:.2f}"
else:
return "No classification found", "N/A"
except requests.exceptions.RequestException as e:
return f"API Error: {e}", ""
except (KeyError, IndexError):
return "Could not parse API response.", ""
finally:
# Clean up the temporary image file
if os.path.exists(image_path):
os.remove(image_path)
# --- Gradio Interface ---
# Define the input and output components
image_input = gr.Image(type="numpy", label="Upload an Image or Use Webcam")
# Define separate outputs for name and confidence
name_output = gr.Label(label="Classification")
confidence_output = gr.Textbox(label="Confidence Score")
# List of example images
example_images = [
["images/img1.jpg"],
["images/img2.jpg"],
["images/img3.jpg"],
["images/img4.jpg"],
["images/img5.jpg"],
]
# Create the Gradio interface
iface = gr.Interface(
fn=classify_image,
inputs=image_input,
outputs=[name_output, confidence_output],
title="Tile Classification: Proof of Concept",
description="Upload a picture or use your camera to classify an image using a pre-trained model. The model's prediction and confidence score will be displayed.",
examples=example_images
)
# Launch the application
iface.launch() |