Upload folder using huggingface_hub

Dockerfile

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+# Use a standard Python 3.9 image
+FROM python:3.9
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file first to leverage Docker caching
+COPY requirements.txt .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application files
+COPY . .
+
+# Command to run the FastAPI application with Uvicorn
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+# Define the FastAPI application
+app = FastAPI()
+
+# Download and load the model
+# Replace "josequinonez/PIMA-Diabetes-Prediction-FastAPI" with your actual Hugging Face repo ID if different
+model_path = hf_hub_download(repo_id="josequinonez/PIMA-Diabetes-Prediction-FastAPI",
+                             filename="best_pima_diabetes_model_v1.joblib", repo_type="model")
+model = joblib.load(model_path)
+
+# Define the input data structure using Pydantic
+class DiabetesFeatures(BaseModel):
+    preg: int
+    plas: int
+    pres: int
+    skin: int
+    test: int
+    mass: float
+    pedi: float
+    age: int
+
+# Define the prediction endpoint
+@app.post("/predict/")
+async def predict_diabetes(features: DiabetesFeatures):
+    # Convert input features to a pandas DataFrame
+    input_data = pd.DataFrame([features.model_dump()])
+
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+
+    # Return the prediction result
+    result = "Diabetic" if prediction == 1 else "Non-Diabetic"
+    return {"prediction": result}
+
+# Optional: Add a root endpoint for testing
+@app.get("/")
+async def read_root():
+    return {"message": "PIMA Diabetes Prediction API is running!"}

requirements.txt

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+pandas==2.2.2
+huggingface_hub==0.32.6
+joblib==1.5.1
+scikit-learn==1.6.0
+fastapi==0.111.0
+uvicorn[standard]==0.30.3
+pydantic==2.7.4