Testing API

Dockerfile

@@ -0,0 +1,20 @@
+# Use the official Python base image
+FROM python:3.10-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install the dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application files
+COPY . .
+
+# Expose the port that Uvicorn will run on
+EXPOSE 7860
+
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -4,6 +4,7 @@ emoji: 🚀
 colorFrom: pink
 colorTo: purple
 sdk: docker
+app_file: app.py
 pinned: false
 short_description: this for testing purpose only.
 ---

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+
+app = FastAPI()
+
+with open("model.pkl") as mdl:
+    model = joblib.load(mdl)
+
+class InputData(BaseModel):
+    sepal_length: float
+    sepal_width: float
+    petal_length: float
+    petal_width: float
+
+@app.get('/')
+def home():
+    return {"Message: This is live API for tesing prediction"}
+
+@app.post("/masala")
+def prediction(data: InputData):
+    features = [[
+        data.sepal_length,
+        data.sepal_width,
+        data.petal_length,
+        data.petal_width
+    ]]
+
+    result = model.predict(features)
+    return {"Prediction": result}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3804676371c6bb25267fdbdc0025d897a24d9cd5205bd0a77ec8b981b1faca48
+size 19409