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katsukiai commited on Mar 8, 2025

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f4e74d7

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1 Parent(s): 3dafe4f

Update app.py

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Files changed (1) hide show

app.py +50 -21

app.py CHANGED Viewed

@@ -5,12 +5,12 @@ import logging
 from tqdm import tqdm
 import gradio as gr
 from transformers import pipeline
-from huggingface_hub import HfApi, upload_file
 # Setup Logging
 logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-# Download NLTK Data
 nltk.download('all')
 # Constants
@@ -40,12 +40,11 @@ def process_text(text):
 def save_to_csv(data, filename="output.csv"):
     with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=["word", "tokenizer", "meanings"])
         writer.writeheader()
         for word in data['words']:
             writer.writerow({
                 "word": word,
-                "tokenizer": data['tokenizer'],
                 "meanings": str(data['meaning'][word])
             })
@@ -64,28 +63,58 @@ def generate_report():
 def generate_all(text):
     data = process_text(text)
     save_to_csv(data)
-    return f"Processed data saved to output.csv"
-iface = gr.Interface(
-    fn=[generate_all, generate_report],
-    inputs="text",
-    outputs=["text", "text"],
-    title="DeepFocus-X3",
-    tab_titles=["Generate All", "Logs"],
-    description="Generate processed data and view logs."
-)
 # Run and Push to HuggingFace
 def run_and_push():
     train_dataset()
-    api = HfApi()
-    api.create_repo(repo_id=HF_REPO, private=False, exist_ok=True)
-    upload_file(
-        path_or_fileobj="output.csv",
-        path_in_repo="output.csv",
-        repo_id=HF_REPO
-    )
-    logging.info("Dataset pushed to HuggingFace.")
 if __name__ == "__main__":
     iface.launch()

 from tqdm import tqdm
 import gradio as gr
 from transformers import pipeline
+from huggingface_hub import HfApi, upload_file, HfFolder
 # Setup Logging
 logging.basicConfig(filename='app.log', level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+# Download All NLTK Data
 nltk.download('all')
 # Constants
 def save_to_csv(data, filename="output.csv"):
     with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
+        writer = csv.DictWriter(csvfile, fieldnames=["word", "meanings"])
         writer.writeheader()
         for word in data['words']:
             writer.writerow({
                 "word": word,
                 "meanings": str(data['meaning'][word])
             })
 def generate_all(text):
     data = process_text(text)
     save_to_csv(data)
+    return "Processed data saved to output.csv"
+# Custom CSS for Tailwind CSS
+custom_css = """
+<head>
+    <link href="https://cdn.jsdelivr.net/npm/tailwindcss@2.2.19/dist/tailwind.min.css" rel="stylesheet">
+</head>
+"""
+with gr.Blocks(css=custom_css) as iface:
+    gr.Markdown("# DeepFocus-X3")
+    with gr.Tab("Generate All"):
+        with gr.Row():
+            input_text = gr.Textbox(label="Input Text", placeholder="Enter your text here...", container=False)
+            output_text = gr.Textbox(label="Output", placeholder="Output will appear here...", container=False)
+            generate_button = gr.Button("Generate", container=False)
+        generate_button.click(fn=generate_all, inputs=input_text, outputs=output_text)
+    with gr.Tab("Logs"):
+        with gr.Row():
+            log_output = gr.Textbox(label="Logs", placeholder="Logs will appear here...", container=False)
+            report_button = gr.Button("Report using Logs", container=False)
+        report_button.click(fn=generate_report, outputs=log_output)
 # Run and Push to HuggingFace
 def run_and_push():
     train_dataset()
+    try:
+        api = HfApi()
+        api.create_repo(repo_id=HF_REPO, private=False, exist_ok=True)
+        upload_file(
+            path_or_fileobj="output.csv",
+            path_in_repo="output.csv",
+            repo_id=HF_REPO
+        )
+        logging.info("Dataset pushed to HuggingFace.")
+    except Exception as e:
+        logging.error(f"Error uploading to HuggingFace: {e}")
+        try:
+            # Log the error to a separate errors repo
+            errors_repo = "katsukiai/errors"
+            api.create_repo(repo_id=errors_repo, private=False, exist_ok=True)
+            with open('upload_error.log', 'w') as error_file:
+                error_file.write(f"Error uploading to HuggingFace: {e}\n")
+            upload_file(
+                path_or_fileobj="upload_error.log",
+                path_in_repo="upload_error.log",
+                repo_id=errors_repo
+            )
+            logging.info("Error log pushed to HuggingFace errors repo.")
+        except Exception as e2:
+            logging.error(f"Failed to log error to HuggingFace errors repo: {e2}")
 if __name__ == "__main__":
     iface.launch()