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.gradio/certificate.pem

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+-----BEGIN CERTIFICATE-----
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README.md

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 ---
-title: MMMInc Testing
-emoji: 🐢
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 6.0.2
+title: MMMInc-testing
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 5.47.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LinearRegression
+import matplotlib.pyplot as plt
+
+# To avoid backend display issues on HF
+plt.switch_backend("Agg")
+
+
+def generate_fake_data():
+    np.random.seed(42)
+    weeks = 104
+
+    data = pd.DataFrame({
+        "week": range(1, weeks + 1),
+        "tv_spend": np.random.gamma(30, 15, weeks),
+        "social_spend": np.random.gamma(20, 20, weeks),
+        "search_spend": np.random.gamma(25, 10, weeks),
+        "price": np.random.normal(10, 0.4, weeks),
+    })
+
+    # true coefficients
+    true_coef = {"tv": 0.8, "social": 1.2, "search": 0.6, "price": -20}
+
+    data["sales"] = (
+        true_coef["tv"] * data["tv_spend"] +
+        true_coef["social"] * data["social_spend"] +
+        true_coef["search"] * data["search_spend"] +
+        true_coef["price"] * data["price"] +
+        np.random.normal(0, 50, weeks)
+    ).round(2)
+
+    return data
+
+
+def run_mmm(data, incremental_increase):
+    X = data[["tv_spend", "social_spend", "search_spend", "price"]]
+    y = data["sales"]
+
+    model = LinearRegression()
+    model.fit(X, y)
+
+    coef = pd.Series(model.coef_, index=X.columns).round(4)
+    intercept = round(model.intercept_, 2)
+
+    # Incrementality
+    social_coef = coef["social_spend"]
+    incremental_sales = round(social_coef * incremental_increase, 2)
+
+    # Plot actual vs predicted
+    data["pred_sales"] = model.predict(X)
+
+    plt.figure(figsize=(8, 4))
+    plt.plot(data["sales"], label="Actual Sales")
+    plt.plot(data["pred_sales"], label="Predicted Sales")
+    plt.legend()
+    plt.title("Actual vs Predicted Sales (MMM)")
+    plt.xlabel("Week")
+    plt.ylabel("Sales")
+
+    plot_path = "plot.png"
+    plt.tight_layout()
+    plt.savefig(plot_path)
+    plt.close()
+
+    return (
+        coef.to_frame("Coefficient").reset_index().rename(columns={"index": "Variable"}),
+        f"Incremental sales from +${incremental_increase} social spend: {incremental_sales}",
+        plot_path
+    )
+
+
+def ui_run(incremental_increase):
+    data = generate_fake_data()
+    coef_table, inc_msg, plot_path = run_mmm(data, incremental_increase)
+    return coef_table, inc_msg, plot_path
+
+
+### -------- Gradio Interface -------- ###
+demo = gr.Interface(
+    fn=ui_run,
+    inputs=[
+        gr.Number(label="Increase in Social Spend (In Dollars)", value=10000)
+    ],
+    outputs=[
+        gr.Dataframe(label="MMM Coefficients"),
+        gr.Textbox(label="Incrementality Result"),
+        gr.Image(label="Actual vs Predicted Sales")
+    ],
+    title="Marketing Mix Model (MMM) + Incrementality",
+    description="A simple MMM that models TV, Social, Search, and Price to predict Sales. Enter an additional Social Ad spend to estimate the incremental uplift.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

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+gradio
+pandas
+numpy
+scikit-learn
+matplotlib