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  1. Dockerfile.txt +9 -0
  2. app.py +44 -0
  3. index.html +19 -0
  4. pneumonia_model.pth +3 -0
  5. requirements.txt +8 -0
  6. script.js +25 -0
  7. style.css +63 -0
Dockerfile.txt ADDED
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+ FROM python:3.10
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+
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+ WORKDIR /app
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+
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+ COPY . /app
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+
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+ RUN pip install --no-cache-dir -r requirements.txt
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+
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+ CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
app.py ADDED
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+ from fastapi import FastAPI, File, UploadFile, Request
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+ from fastapi.responses import HTMLResponse
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+ from fastapi.templating import Jinja2Templates
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+ from fastapi.staticfiles import StaticFiles
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+ from fastapi.middleware.cors import CORSMiddleware
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+ import torch
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+ from torchvision import transforms
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+ from PIL import Image
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+ import io
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+
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+ app = FastAPI()
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+ app.add_middleware(
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+ CORSMiddleware,
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+ allow_origins=["*"],
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+ allow_credentials=True,
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+ allow_methods=["*"],
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+ allow_headers=["*"],
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+ )
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+
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+ app.mount("/static", StaticFiles(directory="static"), name="static")
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+ templates = Jinja2Templates(directory="templates")
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+
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+ model = torch.load("pneumonia_model.pth", map_location=torch.device('cpu'))
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+ model.eval()
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+
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+ transform = transforms.Compose([
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+ transforms.Resize((224, 224)),
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+ transforms.ToTensor(),
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+ ])
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+
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+ @app.get("/", response_class=HTMLResponse)
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+ async def home(request: Request):
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+ return templates.TemplateResponse("index.html", {"request": request})
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+
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+ @app.post("/predict")
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+ async def predict(file: UploadFile = File(...)):
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+ contents = await file.read()
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+ image = Image.open(io.BytesIO(contents)).convert("RGB")
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+ image = transform(image).unsqueeze(0)
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+ with torch.no_grad():
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+ output = model(image)
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+ predicted = torch.argmax(output, 1).item()
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+ result = "Pneumonia" if predicted == 1 else "Normal"
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+ return {"result": result}
index.html ADDED
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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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+ <title>Pneumonia Detection</title>
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+ <link rel="stylesheet" href="/static/style.css">
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+ </head>
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+ <body>
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+ <div class="container">
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+ <h1>Pneumonia Detection from Chest X-ray</h1>
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+ <p class="subtitle">Upload an X-ray image to check for Pneumonia</p>
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+ <input type="file" id="fileInput" accept="image/*">
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+ <button onclick="uploadImage()">Detect</button>
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+ <p id="loading" class="hidden">Processing...</p>
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+ <p id="result"></p>
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+ </div>
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+ <script src="/static/script.js"></script>
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+ </body>
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+ </html>
pneumonia_model.pth ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:677596133caedaa444b61278a31f5507d3f52eee4dc57d902c48fbee22a7101a
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+ size 22
requirements.txt ADDED
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+ fastapi
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+ uvicorn
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+ python-multipart
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+ jinja2
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+ aiofiles
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+ torch
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+ torchvision
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+ Pillow
script.js ADDED
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+ function uploadImage() {
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+ const fileInput = document.getElementById("fileInput");
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+ const file = fileInput.files[0];
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+ if (!file) return;
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+
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+ document.getElementById("loading").classList.remove("hidden");
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+ document.getElementById("result").innerText = "";
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+
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+ const formData = new FormData();
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+ formData.append("file", file);
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+
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+ fetch("/predict", {
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+ method: "POST",
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+ body: formData,
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+ })
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+ .then((res) => res.json())
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+ .then((data) => {
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+ document.getElementById("loading").classList.add("hidden");
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+ document.getElementById("result").innerText = "Prediction: " + data.result;
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+ })
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+ .catch((err) => {
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+ document.getElementById("loading").classList.add("hidden");
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+ document.getElementById("result").innerText = "Error during prediction.";
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+ });
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+ }
style.css ADDED
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+ body {
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+ font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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+ background: linear-gradient(135deg, #d3cce3, #e9e4f0);
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+ margin: 0;
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+ padding: 0;
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+ }
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+
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+ .container {
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+ max-width: 500px;
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+ margin: 80px auto;
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+ background-color: #f0f4f8;
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+ padding: 40px;
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+ border-radius: 15px;
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+ box-shadow: 0 8px 20px rgba(0, 0, 0, 0.15);
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+ text-align: center;
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+ }
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+
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+ h1 {
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+ margin-bottom: 10px;
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+ color: #333;
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+ }
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+
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+ .subtitle {
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+ margin-bottom: 30px;
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+ color: #555;
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+ }
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+
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+ input[type="file"] {
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+ margin: 20px 0;
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+ display: block;
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+ width: 100%;
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+ padding: 10px;
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+ border: 2px dashed #bbb;
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+ border-radius: 10px;
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+ background: #fafafa;
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+ cursor: pointer;
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+ }
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+
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+ button {
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+ background-color: #007bff;
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+ color: white;
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+ border: none;
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+ padding: 12px 24px;
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+ font-size: 16px;
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+ border-radius: 8px;
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+ cursor: pointer;
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+ }
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+
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+ #loading {
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+ margin-top: 20px;
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+ color: #333;
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+ }
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+
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+ #result {
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+ margin-top: 30px;
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+ font-size: 18px;
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+ font-weight: bold;
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+ color: #2c3e50;
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+ }
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+
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+ .hidden {
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+ display: none;
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+ }