Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+catboost_model.cbm filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,17 @@
+# Use official Python image
+FROM python:3.10
+
+# Set working directory
+WORKDIR /app
+
+# Copy all files to container
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Set port Hugging Face expects
+EXPOSE 7860
+
+# Run the FastAPI app on port 9000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "9000"]

README.md

@@ -1,12 +1,24 @@
----
-title: Fastapi Last
-emoji: 😻
-colorFrom: yellow
-colorTo: yellow
-sdk: docker
-pinned: false
-license: mit
-short_description: ml models deploy
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: FastAPI ML API
+emoji: 🚀
+colorFrom: indigo
+colorTo: pink
+sdk: docker
+sdk_version: "1.0"
+app_file: main.py
+pinned: false
+---
+
+# FastAPI ML API
+
+This Space hosts a FastAPI backend for multiple ML models including ANN, XGBoost, Random Forest, and others.
+
+## Features
+- JWT Authentication
+- Predict using multiple ML models
+- Deployed with Docker
+
+## How to Use
+
+1. Get a token using `/token`
+2. Use `/predict/{model}` to get predictions

app.py

@@ -0,0 +1,67 @@
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
+from typing import List
+import joblib
+import tensorflow as tf
+import numpy as np
+from catboost import CatBoostClassifier
+from fastapi.templating import Jinja2Templates
+from fastapi.responses import HTMLResponse
+# App setup
+app = FastAPI()
+templates = Jinja2Templates(directory="templates")
+catboost_model = CatBoostClassifier()
+
+
+@app.get("/", response_class=HTMLResponse)
+def read_index(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+# Input model
+class PredictionInput(BaseModel):
+    features: List[float]
+
+# Load models once
+ann_model = tf.keras.models.load_model("ann_model.keras")
+xgb_model = joblib.load("xgboost.joblib")
+voting_model = joblib.load("voting_classifier.joblib")
+svm_model = joblib.load("svm.joblib")
+rf_model = joblib.load("random_forest.joblib")
+lr_model = joblib.load("logistic_regression (1).joblib")
+catboost_model.load_model("catboost_model.cbm")
+
+# Prediction endpoints (No auth)
+@app.post("/predict/ann")
+def predict_ann(input_data: PredictionInput):
+    prediction = ann_model.predict(np.array([input_data.features]))
+    return {"model": "ANN", "prediction": prediction.tolist()}
+
+@app.post("/predict/xgboost")
+def predict_xgboost(input_data: PredictionInput):
+    prediction = xgb_model.predict([input_data.features])
+    return {"model": "XGBoost", "prediction": prediction.tolist()}
+
+@app.post("/predict/voting")
+def predict_voting(input_data: PredictionInput):
+    prediction = voting_model.predict([input_data.features])
+    return {"model": "VotingClassifier", "prediction": prediction.tolist()}
+
+@app.post("/predict/svm")
+def predict_svm(input_data: PredictionInput):
+    prediction = svm_model.predict([input_data.features])
+    return {"model": "SVM", "prediction": prediction.tolist()}
+
+@app.post("/predict/randomforest")
+def predict_rf(input_data: PredictionInput):
+    prediction = rf_model.predict([input_data.features])
+    return {"model": "RandomForest", "prediction": prediction.tolist()}
+
+@app.post("/predict/logistic")
+def predict_lr(input_data: PredictionInput):
+    prediction = lr_model.predict([input_data.features])
+    return {"model": "LogisticRegression", "prediction": prediction.tolist()}
+
+@app.post("/predict/catboost")
+def predict_catboost(input_data: PredictionInput):
+    prediction = catboost_model.predict([input_data.features])
+    return {"model": "CatBoost", "prediction": prediction.tolist()}

catboost_model.cbm

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+oid sha256:c93fa07ad253661f268a2eb568e1a6b7a65e8ba8829e520b752256111b9e9346
+size 1076972

logistic_regression (1).joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:974435fb5256123aa848decabe0ada5240880129868cb162fd04397354b49143
+size 975

random_forest.joblib

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+oid sha256:36e70c7e66b3c5c45c5fb4f384c7e9464e13e41e51eb08b3cca0f6fd1a509b41
+size 275865

requirements.txt

@@ -0,0 +1,12 @@
+fastapi
+uvicorn[standard]
+joblib
+numpy
+tensorflow
+catboost
+xgboost
+scikit-learn
+python-multipart
+python-jose
+pydantic
+jinja2

static/index.html

@@ -0,0 +1,42 @@
+<!DOCTYPE html>
+<html>
+<head>
+  <title>ML Model Predictor</title>
+</head>
+<body>
+  <h2>📊 Predict</h2>
+  <select id="model">
+    <option value="ann">ANN</option>
+    <option value="xgboost">XGBoost</option>
+    <option value="voting">VotingClassifier</option>
+    <option value="svm">SVM</option>
+    <option value="randomforest">RandomForest</option>
+    <option value="logistic">LogisticRegression</option>
+    <option value="catboost">CatBoost</option>
+  </select><br><br>
+
+  <input id="features" placeholder="Comma-separated features" />
+  <button onclick="predict()">Predict</button>
+
+  <pre id="result"></pre>
+
+  <script>
+    async function predict() {
+      const model = document.getElementById("model").value;
+      const featureString = document.getElementById("features").value;
+      const features = featureString.split(",").map(Number);
+
+      const res = await fetch(`/predict/${model}`, {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json"
+        },
+        body: JSON.stringify({ features })
+      });
+
+      const data = await res.json();
+      document.getElementById("result").textContent = JSON.stringify(data, null, 2);
+    }
+  </script>
+</body>
+</html>

svm.joblib

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+size 12427

voting_classifier.joblib

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xgboost.joblib

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