Update app.py

app.py

@@ -0,0 +1,41 @@
+import json
+import numpy as np
+import gradio as gr
+import keras
+from huggingface_hub import hf_hub_download
+
+REPO_ID = "DrMarcus24/stock-predictor-dev"
+
+# Load architecture from config.json then load weights (.h5)
+cfg_path = hf_hub_download(REPO_ID, "config.json")  # downloads & caches; returns local path
+with open(cfg_path, "r") as f:
+    cfg = json.load(f)
+
+# Keras 3: rebuild from dict, then load weights
+model = keras.saving.deserialize_keras_object(cfg)  # alias: keras.utils.deserialize_keras_object
+w_path = hf_hub_download(REPO_ID, "model.weights.h5")
+model.load_weights(w_path)
+
+def predict_one(csv_line: str):
+    # Expect 9 comma-separated features
+    try:
+        feats = [float(v) for v in csv_line.strip().split(",")]
+    except Exception:
+        raise gr.Error("Parse error. Provide 9 comma-separated numbers.")
+    if len(feats) != 9:
+        raise gr.Error(f"Expected 9 features, got {len(feats)}.")
+    x = np.array(feats, dtype="float32").reshape(1, 1, 9)  # (batch, 1, 9)
+    y = model.predict(x, verbose=0)[0].tolist()           # 7 outputs
+    return {"predictions": y}
+
+demo = gr.Interface(
+    fn=predict_one,
+    inputs=gr.Textbox(placeholder="f1,f2,f3,f4,f5,f6,f7,f8,f9"),
+    outputs="json",
+    title="Stock Predictor (Keras)",
+    description="Enter 9 features; returns 7 predictions."
+)
+
+if __name__ == "__main__":
+    demo.launch()
+