Sync fixed deployment artifacts (corrected f-strings and Dockerfile)

.github/workflows/pipeline.yml

@@ -0,0 +1,18 @@
+name: MLOps Sales Forecasting Pipeline
+on:
+  push:
+    branches: [main]
+jobs:
+  build-and-deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - name: Install dependencies
+        run: pip install -r master/requirements.txt
+      - name: Deploy to HF
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: echo 'Automated deployment logic initialized.'

Dockerfile

@@ -1,20 +1,6 @@
-FROM python:3.13.5-slim
-
+FROM python:3.10
 WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+COPY . .
+RUN pip install -r requirements.txt
+EXPOSE 7860
+CMD ["streamlit","run","app.py","--server.port=7860"]

app.py

@@ -0,0 +1,36 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import os
+from huggingface_hub import hf_hub_download
+
+st.title('SuperKart Sales Forecast App')
+
+REPO_ID = 'Varde001/superkart-sales-model'
+FILENAME = 'best_model.pkl'
+
+@st.cache_resource
+def load_model():
+    try:
+        path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+        return joblib.load(path)
+    except Exception as err:
+        if os.path.exists('master/models/best_model.pkl'):
+            return joblib.load('master/models/best_model.pkl')
+        raise err
+
+try:
+    model = load_model()
+    st.success('Model loaded successfully!')
+except Exception as err:
+    st.error(f'Error loading model: {err}')
+
+file = st.file_uploader('Upload Sales Data (CSV)', type=['csv'])
+if file:
+    df = pd.read_csv(file)
+    if st.button('Predict Sales'):
+        # Filter numerical columns for model input
+        preds = model.predict(df.select_dtypes(include=['number']))
+        df['Predicted_Sales'] = preds
+        st.write(df)

data/test.csv

@@ -0,0 +1,1754 @@
+Product_Id,Product_Weight,Product_Sugar_Content,Product_Allocated_Area,Product_Type,Product_MRP,Store_Id,Store_Establishment_Year,Store_Size,Store_Location_City_Type,Store_Type,Product_Store_Sales_Total
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models/best_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4d5fc459e4c75c4489cdd9a3becf4f9e2929cd65e7ce06970a76e5d39496e435
+size 436497

requirements.txt

@@ -1,3 +1,7 @@
-altair
 pandas
-streamlit
+numpy
+scikit-learn
+xgboost
+joblib
+streamlit
+huggingface_hub