Initial Docker Space deployment

Dockerfile

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+
+FROM python:3.10-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+EXPOSE 7860
+
+CMD ["bash", "-c", "streamlit run app.py --server.address 0.0.0.0 --server.port $PORT"]

app.py

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+import os
+
+os.environ["STREAMLIT_SERVER_ENABLE_CORS"] = "false"
+os.environ["STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION"] = "false"
+import streamlit as st
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# --------------------------
+# Load trained model from Hugging Face
+# --------------------------
+model_repo_id = "Disha252001/tourism-best-model"
+model_file = "best_model.pkl"
+
+local_model_path = hf_hub_download(repo_id=model_repo_id, filename=model_file)
+model = joblib.load(local_model_path)
+
+# --------------------------
+# Input form
+# --------------------------
+with st.form("input_form"):
+
+    Age = st.number_input("Age", min_value=0, max_value=120, value=35)
+    TypeofContact = st.selectbox("Type of Contact", ["Company Invited", "Self Inquiry"])
+    CityTier = st.selectbox("City Tier", [1, 2, 3], index=1)
+    Occupation = st.selectbox("Occupation", ["Salaried", "Freelancer", "Business", "Other"])
+    Gender = st.selectbox("Gender", ["Male", "Female"])
+    NumberOfPersonVisiting = st.number_input("Number Of Person Visiting", min_value=0, value=2)
+    PreferredPropertyStar = st.number_input("Preferred Property Star", min_value=1, max_value=7, value=5)
+    MaritalStatus = st.selectbox("Marital Status", ["Single", "Married", "Divorced"])
+    NumberOfTrips = st.number_input("Number Of Trips (annual)", min_value=0, value=2)
+    Passport = st.selectbox("Passport (0=No,1=Yes)", [0,1], index=1)
+    OwnCar = st.selectbox("Own Car (0=No,1=Yes)", [0,1], index=1)
+    NumberOfChildrenVisiting = st.number_input("Number Of Children Visiting (below 5)", min_value=0, value=0)
+    Designation = st.text_input("Designation", value="Manager")
+    MonthlyIncome = st.number_input("Monthly Income", min_value=0, value=50000)
+    PitchSatisfactionScore = st.number_input("Pitch Satisfaction Score (1-10)", min_value=0, max_value=10, value=8)
+    ProductPitched = st.selectbox("Product Pitched", ["Wellness Package", "Family Package", "Other"])
+    NumberOfFollowups = st.number_input("Number Of Followups", min_value=0, value=1)
+    DurationOfPitch = st.number_input("Duration Of Pitch (minutes)", min_value=0, value=10)
+
+    submitted = st.form_submit_button("Predict")
+
+# --------------------------
+# Convert inputs to DataFrame
+# --------------------------
+def build_input_df():
+    row = {
+        "Age": Age,
+        "TypeofContact": TypeofContact,
+        "CityTier": CityTier,
+        "Occupation": Occupation,
+        "Gender": Gender,
+        "NumberOfPersonVisiting": NumberOfPersonVisiting,
+        "PreferredPropertyStar": PreferredPropertyStar,
+        "MaritalStatus": MaritalStatus,
+        "NumberOfTrips": NumberOfTrips,
+        "Passport": Passport,
+        "OwnCar": OwnCar,
+        "NumberOfChildrenVisiting": NumberOfChildrenVisiting,
+        "Designation": Designation,
+        "MonthlyIncome": MonthlyIncome,
+        "PitchSatisfactionScore": PitchSatisfactionScore,
+        "ProductPitched": ProductPitched,
+        "NumberOfFollowups": NumberOfFollowups,
+        "DurationOfPitch": DurationOfPitch
+    }
+    return pd.DataFrame([row])
+
+# --------------------------
+# Predict and display result
+# --------------------------
+if submitted:
+    input_df = build_input_df()
+    prediction = model.predict(input_df)
+    st.success(f"Predicted ProdTaken: {int(prediction[0])}")

requirements.txt

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+streamlit
+pandas
+scikit-learn
+joblib
+huggingface_hub
+datasets
+