Kortikov Mikhail
commited on
Commit
•
e74a3cd
1
Parent(s):
a3d324f
"Add application file"
Browse files- dockerfile +11 -0
- main.py +7 -0
- metadata.json +1 -0
- models/fine_tuned/resnet50_fruit_classification.pth +3 -0
- models/fruit_recognition_model.pth +3 -0
- requirements.txt +6 -0
- templates/index.html +55 -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", "main:app", "--host", "0.0.0.0", "--port", "7860"]
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main.py
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from fastapi import FastAPI
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app = FastAPI()
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@app.get("/")
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def read_root():
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return {"Hello": "World!"}
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metadata.json
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{"classes": ["apple_braeburn_1", "cucumber_1", "pear_1"]}
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models/fine_tuned/resnet50_fruit_classification.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:ab21975ce41b8e353a3ffbae29f1e7a321584ead64b7f13c8db0d061440a5401
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size 102579565
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models/fruit_recognition_model.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:6fae0b5ba163f4c2f54f3d0c400969b421edf23cfa9c1f129e2ec3e76c7ee173
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size 44792653
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requirements.txt
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fastapi==0.74.*
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requests==2.27.*
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sentencepiece==0.1.*
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torch==1.11.*
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transformers==4.*
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uvicorn[standard]==0.17.*
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templates/index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Fruit Recognition App</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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}
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form {
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display: flex;
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flex-direction: column;
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align-items: center;
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gap: 10px;
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}
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#result {
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font-size: 24px;
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font-weight: bold;
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margin-top: 20px;
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}
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</style>
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</head>
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<body>
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<h1>Fruit Recognition App</h1>
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<form id="fruit-form" enctype="multipart/form-data">
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<label for="image-upload">Choose an image:</label>
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<input type="file" id="image-upload" name="image" accept="image/*" required>
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<button type="submit">Recognize Fruit</button>
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</form>
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<div id="result"></div>
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<script>
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const form = document.getElementById('fruit-form');
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const resultDiv = document.getElementById('result');
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form.addEventListener('submit', async (e) => {
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e.preventDefault();
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const formData = new FormData(form);
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const response = await fetch('/predict/', {
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method: 'POST',
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body: formData
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});
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if (response.ok) {
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const data = await response.json();
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resultDiv.textContent = `Recognized Fruit: ${data.predicted_class}`;
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} else {
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resultDiv.textContent = 'Error: Unable to recognize fruit.';
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}
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});
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</script>
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</body>
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</html>
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