Update app.py requirement.txt Dockerfile

Dockerfile

@@ -7,4 +7,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 
 COPY . /app
 
-CMD ["streamlit", "run", "streamlit_app.py", "--server.port=7860", "--server.address=0.0.0.0"]
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,39 +1,24 @@
 from fastapi import FastAPI
-from pydantic import BaseModel
 import pandas as pd
 import joblib
 from huggingface_hub import hf_hub_download
 
-# --- Load model from HF repo ---
+app = FastAPI()
+
+# Load model from HF repo
 model_path = hf_hub_download(repo_id="danialsiddiqui/task6-model", filename="model.joblib")
 model_data = joblib.load(model_path)
 model = model_data["model"]
 columns = model_data["columns"]
 
-# --- FastAPI app ---
-app = FastAPI(title="Nexus Task6 API")
-
-# Define the input schema
-class PredictInput(BaseModel):
-    gender: str
-    customer_type: str
-    product_line: str
-    unit_price: float
-    quantity: int
-    tax_5: float
-
 @app.get("/")
 def home():
     return {"status": "API is running"}
 
 @app.post("/predict")
-def predict(data: PredictInput):
-    # Convert to DataFrame
-    df = pd.DataFrame([data.dict()])
-    # One-hot encode categorical variables
+def predict(data: dict):
+    df = pd.DataFrame([data])
     df = pd.get_dummies(df)
-    # Reindex to match training columns
     df = df.reindex(columns=columns, fill_value=0)
-    # Predict
     prediction = model.predict(df)[0]
     return {"prediction": float(prediction)}

requirements.txt

@@ -1,8 +1,6 @@
 fastapi
 uvicorn[standard]
-huggingface-hub
-joblib
 pandas
-numpy
 scikit-learn
-streamlit
+joblib
+huggingface-hub