Upload 5 files

Dockerfile

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+# Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
+# you will also find guides on how best to write your Dockerfile
+
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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 ---
-title: Motorbike Accident Prediction
-emoji: 🦀
-colorFrom: blue
-colorTo: red
+title: Accident Prediction
+emoji: 👁
+colorFrom: green
+colorTo: blue
 sdk: docker
 pinned: false
 ---

app.py

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+from fastapi import FastAPI, Request, Form, Depends
+from fastapi.templating import Jinja2Templates
+from fastapi.staticfiles import StaticFiles
+import pickle
+import numpy as np
+
+# Load the trained model
+with open("model.pkl", "rb") as model_file:
+    model = pickle.load(model_file)
+
+app = FastAPI()
+
+# Setup templates and static files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/")
+def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+@app.post("/predict")
+def predict(
+    request: Request,
+    x1: float = Form(...),
+    x2: float = Form(...),
+    x3: float = Form(...),
+    x4: float = Form(...),
+):
+    input_features = np.array([[x1, x2, x3, x4]])
+    prediction = model.predict(input_features)[0]  # Get the predicted category
+    return templates.TemplateResponse("index.html", {"request": request, "result": prediction})

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0dee138c904ac2e2623a1a04609df67e001596738582c398a7716f82a215649a
+size 366219

requirements.txt

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+fastapi
+uvicorn[standard]
+numpy
+jinja2
+scikit-learn
+python-multipart