updating show preds

README copy.md

@@ -1,57 +0,0 @@
----
-title: DIS IPL Predictions
-emoji: 🦀
-colorFrom: red
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.32.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-# DIS IPL Match Predictions App
-
-> "Predict, Compete, and Win 🏏 - Where Every Guess Counts! 🏆"
-
-Welcome to the DIS IPL Match Predictions App! This app allows you to predict the outcomes of IPL matches and compete with your colleagues to win exciting prizes.
-
-## Getting Started
-
-### Prerequisites
-
-- Python 3.x installed on your system
-- Git installed on your system
-- Pip package manager
-
-### Installation
-
-1. Clone this repository to your local machine using Git:
-
-   ```bash
-   git clone <repository_url>
-   ```
-
-2. Navigate to the cloned repository directory:
-
-   ```bash
-   cd ipl-match-predictions-app
-   ```
-
-3. Install the required Python dependencies using Pip:
-
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-### Running The APP
-
-1. After installing the dependencies, you can run the Streamlit app using the following command:
-
-   ```bash
-   streamlit run app.py
-   ```
-
-2. The app will start running locally and open in your default web browser.
-
-Thanks

app.py

@@ -23,12 +23,16 @@ image_path = 'ipl_image.png'
 PREDICTIONS_FOLDER = Path("ipl_predictions")
 PREDICTIONS_FOLDER.mkdir(parents=True, exist_ok=True)
 
+prediction_id = uuid.uuid4().hex
+# Save prediction to a local JSON file for CommitScheduler to handle
+prediction_file_path = f"{PREDICTIONS_FOLDER}/prediction_{prediction_id}.json"
+
 # Initialize CommitScheduler
 scheduler = CommitScheduler(
     repo_id="DIS_IPL_Dataset",
     repo_type="dataset",
     folder_path=PREDICTIONS_FOLDER,  # Local folder where predictions are saved temporarily
-    path_in_repo="predictions",  # Path in dataset repo where predictions will be saved
+    path_in_repo="data",  # Path in dataset repo where predictions will be saved
     every=2,  # Push every 240 minutes (4 hours)
 )
 
@@ -140,9 +144,6 @@ def submit_prediction(
         st.error(f"Your bid points exceed the 20% limit of your total points. Maximum allowed bid points: {max_bid_points}")
         return
 
-
-    # New: Generate a unique ID for this prediction
-    prediction_id = uuid.uuid4().hex
     prediction_date = datetime.now().strftime('%Y-%m-%d')
 
     prediction_data = {
@@ -155,10 +156,10 @@ def submit_prediction(
         'prediction_date': prediction_date  # Include the prediction date
     }
 
-    # Save prediction to a local JSON file for CommitScheduler to handle
-    prediction_file_path = f"{PREDICTIONS_FOLDER}/prediction_{prediction_id}.json"
-    with open(prediction_file_path, 'w') as file:
-        json.dump(prediction_data, file)
+    with scheduler.lock:
+        with prediction_file_path.open("a") as file:
+            file.write(json.dumps(prediction_data))
+            file.write("\n")
 
     st.success("Prediction submitted successfully!")