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Dockerfile

@@ -10,7 +10,7 @@ COPY . .
 # Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-# Define the command to run the Streamlit app on port 8505 and make it accessible externally
-CMD ["streamlit", "run", "app.py", "--server.port=8505", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
 
 # NOTE: Disable XSRF protection for easier external access in order to make batch predictions

app.py

@@ -38,7 +38,7 @@ input_data = pd.DataFrame([{
 }])
 
 # Make prediction when the "Predict" button is clicked
-if st.button("Predict", type='primary'):
+if st.button("Predict"):
     response = requests.post("https://karora1804-StoreTotalSalesPredictionBackend.hf.space/v1/storeSales", json=input_data.to_dict(orient='records')[0])  # Send data to Flask API
     if response.status_code == 200:
         prediction = response.json()['Predicted Total Sales:']

requirements.txt

@@ -1,3 +1,11 @@
 pandas==2.2.2
-requests==2.28.1
+numpy==2.0.2
+requests==2.32.3
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
 streamlit==1.43.2
+gunicorn==20.1.0
+uvicorn[standard]
+huggingface_hub==0.30.1
+Werkzeug==2.2.2