Upload folder using huggingface_hub

Dockerfile

@@ -1,20 +1,23 @@
-FROM python:3.13.5-slim
+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9
 
+# Set the working directory inside the container to /app
 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/
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
 
+# Install Python dependencies listed in requirements.txt
 RUN pip3 install -r requirements.txt
 
-EXPOSE 8501
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+COPY --chown=user . $HOME/app
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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"]

app.py

@@ -0,0 +1,66 @@
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Download and load the model
+model_path = hf_hub_download(repo_id="akskhare/Tourism-Packages", filename="best_tourism-packages_model_v1.joblib")
+model = joblib.load(model_path)
+
+# Streamlit UI for Machine Failure Prediction
+st.title("Tourism Packages App")
+st.write("""
+This application predicts the likelihood of a machine failing based on its operational parameters.
+Please enter the sensor and configuration data below to get a prediction.
+""")
+
+# User input
+Age = st.number_input("Age in Yrs", min_value=1.0, max_value=100.0, step=0.1)
+TypeofContact = st.selectbox("Type of Contact", ['Self Enquiry','Company Contacted'])
+CityTier = st.selectbox("Type of City (Tier1=1,Tier2=2, Tier3=3)", [1,2,3])
+DurationOfPitch = st.number_input("Pitch Time(Hrs)", min_value=0, max_value=1000, step=1)
+Occupation = st.selectbox("Customer Occupation ", ['FreeLancer','Large Business','Salaried','Small Business'])
+Gender = st.selectbox("Gender", ['Male','Female'])
+NumberOfPersonVisiting= st.number_input("Number of Person Visiting", min_value=1, max_value=100, step=1)
+NumberOfFollowups= st.number_input("Number of Followups", min_value=1, max_value=50, step=1)
+ProductPitched= st.selectbox("Product Pitched", ['Basic','Standard','Premium'])
+PreferredPropertyStar= st.number_input("Preferred Property Star", min_value=1, max_value=5, step=1)
+MaritalStatus= st.selectbox("Marital Status", ['Married','Single','Divorced'])
+NumberOfTrips= st.number_input("Number of Trips", min_value=1, max_value=100, step=1)
+Passport= st.selectbox("Passport", ['Yes','No'])
+OwnCar = st.selectbox("Car Owned (Yes=1, No=0)", [1, 0])
+PitchSatisfactionScore= st.number_input("Pitch Satisfaction Score", min_value=1, max_value=5, step=1)
+NumberOfChildrenVisiting= st.number_input("Number of Children Visiting", min_value=0, max_value=10, step=1)
+Designation = st.text_input("Designation")
+MonthlyIncome= st.number_input("Monthly Income:", min_value=1,step=100)
+
+
+# Assemble input into DataFrame
+input_data = pd.DataFrame([{
+    'Age in Yrs': Age,
+    'Type of Contact': TypeofContact,
+    'Type of City (Tier1=1,Tier2=2, Tier3=3)': CityTier,
+    'Pitch Time(Hrs)': DurationOfPitch,
+    'Customer Occupation': Occupation,
+    'Gender': Gender,
+    'Number of Person Visiting': NumberOfPersonVisiting,
+    'Number of Followups': NumberOfFollowups,
+    'Product Pitched': ProductPitched,
+    'Preferred Property Star': PreferredPropertyStar,
+    'Marital Status': MaritalStatus,
+    'Number of Trips': NumberOfTrips,
+    'Passport': Passport,
+    'Car Owned (Yes=1, No=0)': OwnCar,
+    'Pitch Satisfaction Score': PitchSatisfactionScore,
+    'Number of Children Visiting': NumberOfChildrenVisiting,
+    'Designation': Designation,
+    'Monthly Income': MonthlyIncome
+
+}])
+
+
+if st.button("ProdTaken"):
+    prediction = model.predict(input_data)[0]
+    result = "Product Bought" if prediction == 1 else "Not Taken"
+    st.subheader("Prediction Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,7 @@
-altair
-pandas
-streamlit
+pandas==2.2.2
+huggingface_hub==0.36.0
+streamlit==1.52.0
+joblib==1.5.2
+scikit-learn==1.6.1
+xgboost==3.1.2
+mlflow==3.0.1