File size: 3,264 Bytes
ea4eaf4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | import gradio as gr
from transformers import pipeline, CLIPProcessor, CLIPModel
from PIL import Image
import torch
import openai
import base64
import io
# ── 1. DEIN MODELL (von Hugging Face) ──────────────────────────
MY_MODEL_ID = "DEIN-USERNAME/DEIN-MODELL" # ← anpassen!
my_classifier = pipeline("image-classification", model=MY_MODEL_ID)
# ── 2. CLIP (Open-Source) ──────────────────────────────────────
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
# Deine Klassen (anpassen!)
LABELS = ["cat", "dog", "bird"] # ← deine eigenen Klassen
def predict_my_model(image):
results = my_classifier(image)
return {r["label"]: r["score"] for r in results}
def predict_clip(image):
inputs = clip_processor(
text=LABELS, images=image, return_tensors="pt", padding=True
)
with torch.no_grad():
outputs = clip_model(**inputs)
probs = outputs.logits_per_image.softmax(dim=1)[0]
return {label: float(prob) for label, prob in zip(LABELS, probs)}
def predict_openai(image):
client = openai.OpenAI(api_key=openai.api_key)
# Bild zu Base64 konvertieren
buf = io.BytesIO()
image.save(buf, format="JPEG")
b64 = base64.b64encode(buf.getvalue()).decode()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{
"role": "user",
"content": [
{"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{b64}"}},
{"type": "text",
"text": f"Classify this image as one of: {LABELS}. "
f"Return only a JSON like: {{\"label\": score, ...}} "
f"where scores sum to 1."}
]
}],
max_tokens=100
)
import json
return json.loads(response.choices[0].message.content)
def classify_all(image):
r1 = predict_my_model(image)
r2 = predict_clip(image)
r3 = predict_openai(image)
return r1, r2, r3
# ── Beispielbilder ─────────────────────────────────────────────
examples = ["example1.jpg", "example2.jpg"] # ← eigene Bilder
# ── Gradio Interface ───────────────────────────────────────────
with gr.Blocks(title="Image Classification Comparison") as demo:
gr.Markdown("# 🖼️ Image Classification – Model Comparison")
gr.Markdown("Compare your custom model, CLIP, and GPT-4o Vision.")
with gr.Row():
img_input = gr.Image(type="pil", label="Upload Image")
btn = gr.Button("Classify!", variant="primary")
with gr.Row():
out1 = gr.Label(label="🏷️ My Model")
out2 = gr.Label(label="🔍 CLIP")
out3 = gr.Label(label="🤖 GPT-4o")
btn.click(classify_all, inputs=img_input, outputs=[out1, out2, out3])
gr.Examples(examples=examples, inputs=img_input)
demo.launch() |