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Docker

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+
+# Base image
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy everything
+COPY . .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose ports for Flask (5000) and Streamlit (8501)
+EXPOSE 7860
+EXPOSE 7860
+
+# Run both Flask and Streamlit together
+CMD ["bash", "-c", "python model/backend/app.py & streamlit run model/frontend/frontend_app.py --server.port=7860 --server.address=0.0.0.0"]

backend/app.py

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+
+from flask import Flask, request, jsonify
+import joblib
+import numpy as np
+import pandas as pd
+
+app = Flask(__name__)
+
+# Load model
+model = joblib.load("best_dealer_forecast_model.joblib")
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    data = request.json
+    df = pd.DataFrame([data])
+    prediction = model.predict(df)
+    return jsonify({"prediction": prediction.tolist()})

best_dealer_forecast_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a550fe178e06a6473672095d604aee83a1491679836db3c40be51ec8c2c782ec
+size 120528977

frontend/Frontend_app.py

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+
+import streamlit as st
+import requests
+
+st.title("🚗 Dealer Sales Forecast")
+
+dealer_id = st.text_input("Dealer ID")
+variant = st.text_input("Model Variant")
+lag1 = st.number_input("Sales Lag 1", value=0)
+lag2 = st.number_input("Sales Lag 2", value=0)
+lag3 = st.number_input("Sales Lag 3", value=0)
+lag6 = st.number_input("Sales Lag 6", value=0)
+
+if st.button("Predict"):
+    payload = {
+        "dealer_id": dealer_id,
+        "model_variant": variant,
+        "sale_volume_lag_1": lag1,
+        "sale_volume_lag_2": lag2,
+        "sale_volume_lag_3": lag3,
+        "sale_volume_lag_6": lag6
+    }
+    res = requests.post("http://127.0.0.1:7860/predict", json=payload)
+    st.success(f"Prediction: {res.json()['prediction']}")

frontend/app.py

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+
+import streamlit as st
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+
+# Load model
+@st.cache_resource
+def load_model():
+    return joblib.load("model/best_dealer_forecast_model.joblib")
+
+model = load_model()
+
+# Load history
+history = pd.read_csv("dealer_sales_history.csv", parse_dates=["date"])
+
+st.title("🚗 Dealer Variant Sales Forecast")
+
+dealer_id = st.number_input("Enter Dealer ID", min_value=1, step=1)
+variant = st.text_input("Enter Model Variant (e.g., S(O))")
+months = st.slider("Forecast Horizon (months)", 1, 12, 5)
+
+if st.button("Predict"):
+    df = history[
+        (history["dealer_id"] == dealer_id) & 
+        (history["model_variant"] == variant)
+    ].copy().sort_values("date").reset_index(drop=True)
+
+    if df.empty:
+        st.error("No history available for this Dealer + Variant")
+    else:
+        preds = []
+        for i in range(months):
+            df["sale_volume_lag_1"] = df["sale_volume"].shift(1)
+            df["sale_volume_lag_2"] = df["sale_volume"].shift(2)
+            df["sale_volume_lag_3"] = df["sale_volume"].shift(3)
+            df["sale_volume_lag_6"] = df["sale_volume"].shift(6)
+            df["sales_roll_mean_3"] = df["sale_volume"].rolling(3).mean()
+            df["sales_roll_std_3"] = df["sale_volume"].rolling(3).std()
+
+            latest = df.iloc[-1].copy()
+            features = latest[[
+                "dealer_id", "model_variant",
+                "sale_volume_lag_1","sale_volume_lag_2",
+                "sale_volume_lag_3","sale_volume_lag_6",
+                "sales_roll_mean_3","sales_roll_std_3"
+            ]]
+            pred = model.predict(pd.DataFrame([features]))[0]
+            next_date = df["date"].max() + pd.DateOffset(months=1)
+            preds.append({"date": next_date, "predicted_sales": pred})
+
+            df = pd.concat([df, pd.DataFrame({
+                "date": [next_date],
+                "dealer_id": [dealer_id],
+                "model_variant": [variant],
+                "sale_volume": [pred]
+            })], ignore_index=True)
+
+        forecast = pd.DataFrame(preds)
+        st.write("### Forecast Results", forecast)
+
+        fig, ax = plt.subplots()
+        ax.plot(forecast["date"], forecast["predicted_sales"], marker="o", label="Forecast")
+        ax.set_xlabel("Date")
+        ax.set_ylabel("Predicted Sales")
+        st.pyplot(fig)