first try

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# --------------------------------------------------------------------------
+#  1. Load your NER model from the Hub using a pipeline
+# --------------------------------------------------------------------------
+# Replace this with your actual model name on the Hugging Face Hub
+model_name = "swardiantara/ADFLER-xlnet-base-cased" 
+ner_pipeline = pipeline("ner", model=model_name, aggregation_strategy="simple")
+
+# --------------------------------------------------------------------------
+#  2. Define the prediction function
+# --------------------------------------------------------------------------
+# This function takes raw text and returns a format that Gradio's HighlightedText component understands.
+def recognize_log_events(text):
+    """
+    Performs NER on the input text and formats the output for Gradio.
+    """
+    if not text:
+        return {"text": "", "entities": []}
+        
+    ner_results = ner_pipeline(text)
+    
+    # Format the results for the HighlightedText component
+    # It expects a list of tuples: (word, entity_label)
+    # The pipeline with aggregation_strategy="simple" provides this almost directly.
+    entities = []
+    for result in ner_results:
+        entities.append((result['entity_group'], result['word']))
+    
+    # Gradio's HighlightedText component works best with a dictionary
+    # containing the original text and the list of entities.
+    # We will return the text split by spaces and the corresponding entities.
+    words = text.split()
+    highlighted_output = []
+    
+    # This is a simple way to tag words. More complex logic may be needed
+    # if an entity spans multiple words that are not contiguous.
+    # For simplicity, we create a lookup for recognized words.
+    entity_lookup = {entity[1].strip(): entity[0] for entity in entities}
+
+    for word in words:
+        label = entity_lookup.get(word)
+        highlighted_output.append((word, label))
+
+    return highlighted_output
+
+# --------------------------------------------------------------------------
+#  3. Create the Gradio Interface
+# --------------------------------------------------------------------------
+# A brief description of your project to display in the app
+description = """
+This demo showcases an NER model for recognizing key events in drone flight logs, 
+a part of my PhD research in digital forensics at ITS. 
+Enter a line from a drone log to see the model identify events like 'Takeoff', 'Landing', 'GPS Lock', etc.
+"""
+
+# An article providing more context and links
+article = """
+<div style='text-align: center;'>
+    <p>For more details, check out the project on GitHub:</p>
+    <a href='https://dronenlp.github.io/documentation' target='_blank'>DroneNLP Project</a> | 
+    <a href='https://huggingface.co/swardiantara/ADFLER-xlnet-base-cased' target='_blank'>Model Card</a> |
+</div>
+"""
+# <a href='https' target='_blank'>Research Paper (if applicable)</a>
+
+# Example drone log entries for users to try
+examples = [
+    ["No image transmission. RTH.; Press Brake button to cancel RTH.; No image transmission. Aircraft returning to home.; Image transmission signal weak. Adjust antennas and make sure they are perpendicular to flight direction of aircraft.; Flight mode changed to Go Home."],
+    ["Battery temperature is below 15 degrees Celsius. Warm up the battery temperature to above 25 degree Celsius to ensure a safe flight."],
+    ["Strong wireless interference. Please fly with caution. Obstacle Avoidance Disabled. Landing gear lowered. Obstacle Avoidance Disabled."],
+    ["Cannot switch flight mode. Turn on 'Multiple Flight Modes' to enable Atti and Sport Modes."]
+]
+
+# The main interface
+iface = gr.Interface(
+    fn=recognize_log_events,
+    inputs=gr.Textbox(
+        lines=5, 
+        label="Drone Flight Log Entry", 
+        placeholder="Paste a log entry here..."
+    ),
+    outputs=gr.HighlightedText(
+        label="Recognized Events",
+        color_map={"Event": "green"} # Customize these labels and colors!
+    ),
+    title="🚁 Drone Flight Log Event Recognizer",
+    description=description,
+    article=article,
+    examples=examples
+)
+
+# Launch the app!
+iface.launch()

requirements.txt

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+gradio
+transformers[torch]
+sentencepiece