App first version

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: Fastapi First
+emoji: 🌍
+colorFrom: purple
+colorTo: pink
+sdk: docker
+pinned: false
+license: mit
+short_description: A space for fastapi endpoint
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from typing import Union
+import os
+import requests
+import json
+import time
+from datetime import datetime
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+# Configure CORS settings
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["https://react-first-tan.vercel.app/"],  # List of allowed origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allow all HTTP methods
+    allow_headers=["*"],  # Allow all headers
+)
+
+from dotenv import load_dotenv, find_dotenv
+_ = load_dotenv(find_dotenv()) # read local .env file
+
+@app.get("/")
+def read_root():
+    return {"Hello": "World"}
+
+@app.get("/get_prediction")
+def get_prediction_from_jobrun():
+    #Add the documentation
+    """
+    Get the prediction from the Databricks job run
+    """
+    # Replace these variables with your Databricks workspace information
+    DATABRICKS_INSTANCE = 'https://2461626258595269.9.gcp.databricks.com'
+    API_TOKEN = os.getenv('API_TOKEN')
+    TASK_RUNID = '1054089068841244'
+
+    url = f"{DATABRICKS_INSTANCE}/api/2.1/jobs/runs/get-output"
+    headers = {
+        'Authorization': f'Bearer {API_TOKEN}',
+        'Content-Type': 'application/json'
+    }
+    data = {
+        "run_id": TASK_RUNID
+    }
+
+    #cert_path = r"C:\Users\PD817AE\OneDrive - EY\Desktop\mdlz\pipeline_code\Zscaler Root CA.crt"
+    response = requests.get(url, headers=headers, data=json.dumps(data))
+
+    if response.status_code == 200:
+        print("Pipeline run initiated successfully.")
+        output_json = json.loads(response.json()['notebook_output']['result'])
+        nb_output = output_json['prediction']
+        return nb_output
+    else:
+        print(response)
+        print("Failed to initiate pipeline run.")
+        print("Status Code:", response.status_code)
+        return response.text
+
+@app.get("/get_prediction_from_databricks")
+def run_xpipeline():
+    print(f"Running the pipeline : {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ")
+    access_token_dev = os.getenv('API_TOKEN')
+    headers = {
+        "Authorization": f"Bearer {access_token_dev}",
+        "Content-Type": "application/json"
+    }
+    # Pipeline details
+
+    pipeline_id = "413640122908266"  # "326843486210150"
+    #sample_df = pd.read_csv("wine_test_dataset.csv")
+    #print(f"============= Input Data ============")
+    #print(sample_df)
+    #print(f"=====================================")
+
+    # Convert the DataFrame to JSON
+    json_data = None #sample_df.to_json()
+    payload = {
+        'job_id': pipeline_id,
+        'notebook_params': {
+            'data': json_data  # Send data as a JSON string
+        }
+    }
+
+    # Trigger the run
+    api_url = f"https://2461626258595269.9.gcp.databricks.com/api/2.1/jobs/run-now"
+    response = requests.post(api_url, headers=headers, data=json.dumps(payload))
+    response_json = response.json()
+    print(f"\nPrediction pipeline started with details : {response_json}\n")
+    run_id = response_json["run_id"]
+    #pred_out = pd.DataFrame()
+    while True:
+        time.sleep(2)
+        api_url = f"https://2461626258595269.9.gcp.databricks.com/api/2.1/jobs/runs/get?run_id={run_id}"
+        response = requests.get(api_url, headers=headers)
+        response_json = response.json()
+        task_run_id = response_json['tasks'][0]['run_id']
+        run_status = response_json["state"]["life_cycle_state"]
+        print(f"{datetime.now().strftime('%Y-%m-%d %H:%M:%S')} Status : {run_status}")
+        job_status = response_json["state"].get('result_state')
+        if job_status == 'SUCCESS':
+            api_url = f"https://2461626258595269.9.gcp.databricks.com/api/2.1/jobs/runs/get-output"
+            payload = dict(run_id=task_run_id)
+            response = requests.get(api_url, headers=headers, data=json.dumps(payload))
+            #response_dict = response.json()
+            output_json = json.loads(response.json()['notebook_output']['result'])
+            nb_output = output_json['prediction']
+            #notebook_output = json.loads(response_dict["notebook_output"]["result"])
+            break;
+            #pred_out = pd.DataFrame(notebook_output)
+            #break
+
+    return nb_output
+
+@app.get("/items/{item_id}")
+def read_item(item_id: int, q: Union[str, None] = None):
+    return {"item_id": item_id, "q": q}

requirements.txt

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+fastapi
+requests
+uvicorn[standard]