Create app.py

app.py

@@ -0,0 +1,93 @@
+# app.py
+import gradio as gr
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from inference import infer  # make sure inference.py is in the same folder
+
+def parse_prices(text, csv_file):
+    # Priority: uploaded CSV > pasted text
+    if csv_file is not None:
+        try:
+            df = pd.read_csv(csv_file.name)
+            if "Close" in df.columns:
+                prices = df["Close"].dropna().astype(float).tolist()
+                return prices
+            # if no Close column fall back to first numeric column
+            for c in df.columns:
+                if pd.api.types.is_numeric_dtype(df[c]):
+                    return df[c].dropna().astype(float).tolist()
+            return []
+        except Exception as e:
+            return []
+    if text:
+        # accept comma or newline separated floats
+        tokens = [t.strip() for t in text.replace("\n", ",").split(",") if t.strip() != ""]
+        try:
+            return [float(t) for t in tokens]
+        except:
+            return []
+    return []
+
+def run_forecast(model_type, prices_text, csv_file, steps, epochs, plot_history_len):
+    prices = parse_prices(prices_text, csv_file)
+    if not prices:
+        return "ERROR: No valid input prices found. Upload a CSV with a Close column or paste comma-separated prices.", None
+
+    # ensure list length is reasonable
+    if len(prices) < 2 and model_type.lower() == "arima":
+        return "ERROR: Need at least 2 prices for ARIMA.", None
+
+    # Call infer (inference.infer should accept epochs param)
+    try:
+        preds = infer(model_type, prices, steps=steps, epochs=epochs)
+    except Exception as e:
+        return f"ERROR during inference: {e}", None
+
+    # Build a simple plot: last N history points + forecast points
+    hist_len = min(plot_history_len, len(prices))
+    hist_x = list(range(-hist_len, 0))
+    hist_y = prices[-hist_len:]
+
+    forecast_x = list(range(0, len(preds)))
+    forecast_y = preds
+
+    fig, ax = plt.subplots(figsize=(8,4))
+    ax.plot(hist_x, hist_y, marker="o", label="History (last {})".format(hist_len))
+    # plot forecast continuing after history
+    ax.plot([hist_x[-1]] + [hist_x[-1] + i + 1 for i in forecast_x],
+            [hist_y[-1]] + forecast_y, marker="o", linestyle="--", label="Forecast")
+    ax.set_xlabel("Time (relative)")
+    ax.set_ylabel("Price")
+    ax.legend()
+    ax.grid(True)
+    plt.tight_layout()
+
+    # return predictions (list) and the matplotlib figure
+    preds_text = {"model": model_type, "predictions": preds}
+    return str(preds_text), fig
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Stock Forecast — ARIMA vs LSTM\nUpload a CSV with a `Close` column or paste comma-separated closing prices.")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            model_type = gr.Radio(choices=["arima", "lstm"], value="arima", label="Model")
+            steps = gr.Slider(minimum=1, maximum=30, step=1, value=5, label="Forecast steps")
+            epochs = gr.Slider(minimum=1, maximum=100, step=1, value=5, label="LSTM training epochs (only used for LSTM)")
+            plot_history_len = gr.Slider(minimum=10, maximum=500, step=10, value=100, label="History length to plot")
+
+            csv_file = gr.File(label="Upload CSV (optional, must include Close column)")
+            prices_text = gr.Textbox(lines=4, placeholder="Or paste comma-separated prices (e.g. 100,101.5,102)", label="Paste prices (optional)")
+
+            run_btn = gr.Button("Run forecast")
+        with gr.Column(scale=1):
+            output_text = gr.Textbox(label="Predictions (JSON string)")
+            output_plot = gr.Plot(label="History + Forecast Plot")
+
+    run_btn.click(fn=run_forecast,
+                  inputs=[model_type, prices_text, csv_file, steps, epochs, plot_history_len],
+                  outputs=[output_text, output_plot])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)