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 all the files from the current directory on the host to the container's /app directory
+COPY . .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+#Install python dependencies listed in requirements.text
+RUN pip install -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+RUN useradd -m -u 1000 user
+USER user
+ENV HOME=/home/user \
+   PATH=/home/user/.local/bin:$PATH
 
-EXPOSE 8501
+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 itaccessible externally
+CMD ["streamlit","run","app.py","--server.port=8501", "--server.address=0.0.0.0","--server.enableXsrfProtection-False" "]

app.py

@@ -0,0 +1,64 @@
+import streamlit as st
+import requests
+import json
+import pandas as pd
+from huggingface_hub import hf_hub_download # Corrected import statement
+import joblib # Corrected typo
+
+#Download and load the model
+model_path = hf_hub_download(repo_id="grkavi0912/Tpro", filename="best_tour_model.joblib", repo_type="model") # Added repo_type
+model = joblib.load(model_path)
+
+#Streamlit UI for Tourism package prediction
+st.title("Tourism Package Prediction")
+st.write("Enter the details to predict the package price")
+
+#User input
+Age = st.number_input("Age",min_value=18,max_value=100)
+Type_of_contact = st.selectbox("Type of Contact",["Direct","Call"])
+City_Tier = st.selectbox("City Tier",[1,2,3])
+Duration_of_Pitch = st.number_input("Duration of Pitch",min_value=1,max_value=365)
+Occupation = st.selectbox("Occupation",["Self-employed","Salaried","Business"])
+Gender = st.selectbox("Gender",["Male","Female"])
+Number_of_Person_Visiting= st.number_input("Number of Person Traveling",min_value=1,max_value=10)
+Number_of_Followups= st.number_input("Number of Followups",min_value=0,max_value=10)
+Product_Pitched= st.selectbox("Product Pitched",["Basic","Standard","Premium"])
+Preferred_Property_Star= st.number_input("Preferred Property Star",min_value=1,max_value=5)
+Marital_Status= st.selectbox("Marital Status",["Married","Divorced","Single"])
+Number_of_Trips= st.number_input("Number of Trips",min_value=1,max_value=10)
+Passport= st.selectbox("Passport",["Yes","No"])
+Pitch_Satisfaction_Score= st.number_input("Pitch Satisfaction Score",min_value=1,max_value=5)
+Own_Car= st.selectbox("Own Car",["Yes","No"])
+Number_of_Children= st.number_input("Number of Children",min_value=0,max_value=10)
+Designation= st.selectbox("Designation",["Executive","Manager","Senior Manager","Associate","Director"])
+Monthly_Income= st.number_input("Monthly Income",min_value=0,max_value=100000)
+
+#Assemble input into DataFrame
+input_data = pd.DataFrame({
+    "Age": [Age],
+    "TypeofContact": [Type_of_contact], # Corrected variable name
+    "CityTier": [City_Tier], # Corrected variable name
+    "DurationOfPitch": [Duration_of_Pitch], # Corrected variable name
+    "Occupation": [Occupation],
+    "Gender": [Gender],
+    "NumberOfPersonVisiting": [Number_of_Person_Visiting], # Corrected variable name
+    "NumberOfFollowups": [Number_of_Followups], # Corrected variable name
+    "ProductPitched": [Product_Pitched], # Corrected variable name
+    "PreferredPropertyStar": [Preferred_Property_Star], # Corrected variable name
+    "MaritalStatus": [Marital_Status], # Corrected variable name
+    "NumberOfTrips": [Number_of_Trips], # Corrected variable name
+    "Passport": [1 if Passport == "Yes" else 0], # Converted to numerical
+    "PitchSatisfactionScore": [Pitch_Satisfaction_Score], # Corrected variable name
+    "OwnCar": [1 if Own_Car == "Yes" else 0], # Converted to numerical
+    "NumberOfChildrenVisiting": [Number_of_Children], # Corrected variable name
+    "Designation": [Designation],
+    "MonthlyIncome": [Monthly_Income]
+
+})
+
+if st.button("Predict"):
+    #Make prediction
+    prediction = model.predict(input_data)[0]
+    result = "Tourism package predicted as " + str(prediction)
+    st.subheader("Predicted Result:")
+    st.success(f"The model predicts: **{result}**")

requirements.txt

@@ -1,3 +1,8 @@
-altair
-pandas
-streamlit
+pandas==2.2.as_integer_ratio
+huggingface_hub==0.32.6
+streamlit==1.43.2
+joblib==1.5.1
+scikit-learn==1.6.0
+xgboost==2.1.4
+mlflow==3.0.1
+