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Browse files- Dockerfile +11 -0
- app.py +50 -0
- requirements.txt +7 -0
Dockerfile
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FROM python:3.9
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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COPY . .
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CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
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app.py
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from flask import Flask, request, jsonify, render_template
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from transformers import AutoFeatureExtractor, AutoModelForImageClassification
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from PIL import Image
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import requests
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import torch
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# Initialize Flask app
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app = Flask(__name__)
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# Load pre-trained model and feature extractor
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feature_extractor = AutoFeatureExtractor.from_pretrained('karan99300/ConvNext-finetuned-CIFAR100')
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model = AutoModelForImageClassification.from_pretrained('karan99300/ConvNext-finetuned-CIFAR100')
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# Define route for home page with form
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@app.route('/', methods=['GET', 'POST'])
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def index():
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if request.method == 'POST':
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# Get image URL from form submission
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image_url = request.form['image_url']
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# Classify image
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predicted_class = classify_image(image_url)
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return render_template('index.html', predicted_class=predicted_class, image_url=image_url)
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return render_template('index.html')
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# Function to classify image
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def classify_image(image_url):
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# Fetch image from URL
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try:
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image = Image.open(requests.get(image_url, stream=True).raw)
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except Exception as e:
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return f'Error fetching image: {str(e)}'
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# Preprocess image and perform inference
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pixel_values = feature_extractor(image.convert('RGB'), return_tensors='pt').pixel_values
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with torch.no_grad():
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outputs = model(pixel_values)
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logits = outputs.logits
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predicted_class_idx = logits.argmax(-1).item()
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# Get predicted label
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predicted_label = model.config.id2label[predicted_class_idx]
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return predicted_label
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# Run Flask app
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if __name__ == '__main__':
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app.run(debug=True,port=5000)
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requirements.txt
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flask
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numpy
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Pillow
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torch
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transformers
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requests
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uvicorn
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