changed gradio interface to block

app.py

@@ -50,7 +50,6 @@ from pytz import timezone
 from pandas.tseries.offsets import BDay
 import hashlib
 
-
 import time
 import gc
 
@@ -61,8 +60,6 @@ lock = threading.Lock()
 
 # --- Load saved model and scalers --- #
 model_dir = "./model"
-# import os
-# model_dir = os.path.join(os.path.dirname(__file__), "model")
 
 NN_model = load_model(os.path.join(model_dir, "NN_CPU_model.keras"))
 
@@ -88,53 +85,53 @@ def safe_download(*args, retries=3, delay=1, **kwargs):
         time.sleep(delay)
     raise RuntimeError("yfinance download failed after retries.")
 
-lock = threading.Lock() 
-
 # --- Inference Function --- #
 def predict_stock():
-    
-    # lightweight log print near the top of predict_stock() to verify it's hitting the cache config:
-    print("YFINANCE_NO_CACHE =", os.getenv("YFINANCE_NO_CACHE"))
-
-    # Check for time zone
-    now_est = datetime.now(timezone("US/Eastern"))
-    print("Current Eastern Time:", now_est)
-    print("Trying to fetch data up to:", now_est.strftime('%Y-%m-%d'))
-
-    # --- Clear yfinance cache to get latest volume and price data --- #
-    cache_path = os.path.expanduser("~/.cache/py-yfinance")
-    if os.path.exists(cache_path):
-        print("Clearing yfinance cache...")
-        shutil.rmtree(cache_path)
-
-    Stock = "NVDA"
-    start_date = "2020-01-01"
-    train_end_date = "2024-12-31"
-    #today = datetime.today().strftime('%Y-%m-%d')
-    # Use EST for consistently for today
-    today = now_est.strftime('%Y-%m-%d')
-
-    # Download the full dataset (might contain stale final row)
-    # solves any error with empty dataframes
-    try:
-        full_data = safe_download(
-            tickers=Stock,
-            start=start_date,
-            end=today,
-            interval="1d",
-            auto_adjust=False,
-            actions=False,
-            progress=False,
-            threads=True        #<-- for parallel downloads, use True
-        )
-
-        if full_data.empty:
-            print("yfinance returned empty data for:", today)
-            return "Error: Stock data not available at this time. Please try again shortly.", pd.DataFrame()
-
-    except Exception as e:
-        print("yfinance error:", e)
-        return "Error: Could not fetch stock data. Please try again later.", pd.DataFrame()
+
+    with lock:
+
+        # lightweight log print near the top of predict_stock() to verify it's hitting the cache config:
+        print("YFINANCE_NO_CACHE =", os.getenv("YFINANCE_NO_CACHE"))
+
+        # Check for time zone
+        now_est = datetime.now(timezone("US/Eastern"))
+        print("Current Eastern Time:", now_est)
+        print("Trying to fetch data up to:", now_est.strftime('%Y-%m-%d'))
+
+        # --- Clear yfinance cache to get latest volume and price data --- #
+        cache_path = os.path.expanduser("~/.cache/py-yfinance")
+        if os.path.exists(cache_path):
+            print("Clearing yfinance cache...")
+            shutil.rmtree(cache_path)
+
+        Stock = "NVDA"
+        start_date = "2020-01-01"
+        train_end_date = "2024-12-31"
+        #today = datetime.today().strftime('%Y-%m-%d')
+        # Use EST for consistently for today
+        today = now_est.strftime('%Y-%m-%d')
+
+        # Download the full dataset (might contain stale final row)
+        # solves any error with empty dataframes
+        try:
+            full_data = safe_download(
+                tickers=Stock,
+                start=start_date,
+                end=today,
+                interval="1d",
+                auto_adjust=False,
+                actions=False,
+                progress=False,
+                threads=True        #<-- for parallel downloads, use True
+            )
+
+            if full_data.empty:
+                print("yfinance returned empty data for:", today)
+                return "Error: Stock data not available at this time. Please try again shortly.", pd.DataFrame()
+
+        except Exception as e:
+            print("yfinance error:", e)
+            return "Error: Could not fetch stock data. Please try again later.", pd.DataFrame()
 
     features = ["Open", "High", "Low", "Close", "Volume"]
 
@@ -198,9 +195,10 @@ def predict_stock():
 
     headers = ["Prediction For Date", "Actual Close", "Predicted Close", "% Error", "±MAPE Range"]
     table = tabulate(prediction_df.values, headers=headers, tablefmt="plain")
-    
-    """
+
     # Start Sanity Checks
+
+
     assert not np.any(np.isnan(X_scaled[-1].reshape(1, -1))), "NaNs detected in input!"
     assert X_scaled[-1].reshape(1, -1).shape == (1, X_scaled.shape[1]), f"Unexpected shape: {X_scaled[-1].reshape(1, -1).shape}"
     print("X_input shape:", X_scaled[-1].reshape(1, -1).shape)
@@ -221,21 +219,19 @@ def predict_stock():
     print("Debug prediction (scaled):", y_debug)
     print("Debug prediction (unscaled):", y_debug_unscaled)
 
+    gc.collect()        #<-- garbage collection after predict
+
     import hashlib
 
     def md5(fname):
         with open(fname, "rb") as f:
             return hashlib.md5(f.read()).hexdigest()
 
-    #print("Model MD5 checksum:", md5(os.path.join(model_dir, "NN_CPU_model.keras")))
+    print("Model MD5 checksum:", md5(os.path.join(model_dir, "NN_CPU_model.keras")))
 
     print(full_data.tail(3))
 
-    # End Sanitiy Checks"
-    """
-
-    # Prints in log and helps to verify any issues with yfinance downloads
-    print("Attempting to fetch data from", start_date, "to", today)
+    # End Sanitiy Checks
 
     return summary, prediction_df[["Date", "Actual Close", "Predicted Close", "% Error", "±MAPE Range"]]
 
@@ -255,7 +251,7 @@ demo = gr.Interface(
     live=True       #<-- changed to True for live queuing
 )
 
-demo.launch(share=True)
+demo.launch()
 """
 
 with gr.Blocks() as demo: