Update app.py

app.py

@@ -1,23 +1,60 @@
 import gradio as gr
 from transformers import pipeline
+import re
 
-# Load a pre-trained AI text detector model
+# Load the pre-trained AI text classification model
 detector = pipeline("text-classification", model="roberta-base-openai-detector")
 
+# Count words in the input text
+def count_words(text):
+    return len(re.findall(r'\b\w+\b', text))
+
+# Count characters (excluding spaces)
+def count_characters(text):
+    return len(text.replace(" ", ""))
+
+# Detect if the text is AI-generated or human-written
 def detect_text(text):
+    if not text.strip():
+        return "No text entered."
     result = detector(text)[0]
     label = result['label']
     score = round(result['score'] * 100, 2)
     return f"Prediction: {label} ({score}%)"
 
-# Gradio UI
-interface = gr.Interface(
-    fn=detect_text,
-    inputs=gr.Textbox(lines=7, label="Enter your text"),
-    outputs=gr.Textbox(label="Result"),
-    title="Text AI Detector",
-    description="This model predicts if the text is AI-generated or human-written."
-)
+# Perform full analysis
+def full_analysis(text):
+    prediction = detect_text(text)
+    words = count_words(text)
+    chars = count_characters(text)
+    return f"{prediction}\n\nWord Count: {words}\nCharacter Count: {chars}"
+
+description = """
+Detect whether a given text is AI-generated or human-written.
+Also view word and character count for basic analysis.
+"""
+
+examples = [
+    ["The sun sets beautifully behind the hills every evening."],
+    ["As an AI language model developed by OpenAI, I am capable of many tasks."],
+    ["She opened the book and smiled as the story unfolded."]
+]
+
+with gr.Blocks(title="Text AI Detector") as interface:
+    gr.Markdown("# Text AI Detector")
+    gr.Markdown(description)
+
+    with gr.Tab("Detector"):
+        text_input = gr.Textbox(label="Input Text", lines=8, placeholder="Type or paste your text here...")
+        analyze_btn = gr.Button("Analyze")
+        output = gr.Textbox(label="Result", lines=6)
+        analyze_btn.click(fn=full_analysis, inputs=text_input, outputs=output)
+
+    with gr.Tab("Examples"):
+        gr.Examples(examples=examples, inputs=[text_input], outputs=[output], fn=full_analysis)
 
-interface.launch()
+    gr.Markdown("---")
+    gr.Markdown("Final Year Project | Built with Hugging Face + Gradio")
 
+if __name__ == "__main__":
+    interface.launch()