Update app.py

app.py

@@ -3,39 +3,25 @@ from gensim.models import TfidfModel
 from gensim.corpora import Dictionary
 from gensim.utils import simple_preprocess
 from gensim.parsing.preprocessing import remove_stopwords
-import numpy as np
-import warnings
-warnings.filterwarnings('ignore')
 
 # Example texts
 EXAMPLES = {
     "Scientific Abstract": """
     Compatibility of systems of linear constraints over the set of natural numbers. 
     Criteria of compatibility of a system of linear Diophantine equations, strict inequations, 
-    and nonstrict inequations are considered. Upper bounds for components of a minimal set of solutions 
-    and algorithms of construction of minimal generating sets of solutions for all types of systems are given.
+    and nonstrict inequations are considered.
     """,
     "News Article": """
     Machine learning is revolutionizing the way we interact with technology. 
     Artificial intelligence systems are becoming more sophisticated, enabling automated decision making 
-    and pattern recognition at unprecedented scales. Deep learning algorithms continue to improve, 
-    making breakthroughs in natural language processing and computer vision.
+    and pattern recognition at unprecedented scales.
     """,
     "Technical Documentation": """
     The user interface provides intuitive navigation through contextual menus and adaptive layouts. 
-    System responses are optimized for performance while maintaining high reliability standards. 
-    Database connections are pooled to minimize resource overhead and maximize throughput.
+    System responses are optimized for performance while maintaining high reliability standards.
     """
 }
 
-def preprocess_text(text):
-    # Remove stopwords
-    text = remove_stopwords(text)
-    # Tokenize and clean text
-    tokens = simple_preprocess(text, deacc=True)
-    return ' '.join(tokens)
-
-# Initialize text processing components
 def extract_keywords(text, num_keywords=10, scores=True, min_length=1):
     # Preprocess text
     processed_text = remove_stopwords(text.lower())
@@ -66,64 +52,26 @@ def extract_keywords(text, num_keywords=10, scores=True, min_length=1):
             
     return "\n".join(results) if results else "No keywords found."
 
-# Update the interface click handler to match the function parameters
-extract_btn.click(
-    extract_keywords,
-    inputs=[input_text, num_keywords, show_scores, min_length],
-    outputs=[output_text]
-)
-
 def load_example(example_name):
     return EXAMPLES.get(example_name, "")
 
 # Create Gradio interface
-with gr.Blocks(title="Keyword Extraction") as demo:
-    gr.Markdown("# 📑 Keyword Extraction")
-    
-    with gr.Row():
-        with gr.Column(scale=2):
-            input_text = gr.Textbox(
-                label="Input Text",
-                placeholder="Enter your text here...",
-                lines=8
-            )
-            
-        with gr.Column(scale=1):
-            num_keywords = gr.Slider(
-                minimum=1,
-                maximum=20,
-                value=10,
-                step=1,
-                label="Number of Keywords"
-            )
-            
-            show_scores = gr.Checkbox(
-                label="Show Scores",
-                value=True
-            )
-            
-            min_length = gr.Slider(
-                minimum=1,
-                maximum=5,
-                value=1,
-                step=1,
-                label="Minimum Words per Keyword"
-            )
-            
-            # Define the button here, before using it
-            extract_btn = gr.Button("Extract Keywords", variant="primary")
-    
-    output_text = gr.Textbox(
-        label="Extracted Keywords",
-        lines=10,
-        interactive=False
-    )
-    
-    # Add the click event after the button is defined
-    extract_btn.click(
-        fn=extract_keywords,
-        inputs=[input_text, num_keywords, show_scores, min_length],
-        outputs=output_text
-    )
+demo = gr.Interface(
+    fn=extract_keywords,
+    inputs=[
+        gr.Textbox(lines=8, label="Input Text", placeholder="Enter your text here..."),
+        gr.Slider(minimum=1, maximum=20, value=10, step=1, label="Number of Keywords"),
+        gr.Checkbox(label="Show Scores", value=True),
+        gr.Slider(minimum=1, maximum=5, value=1, step=1, label="Minimum Words per Keyword")
+    ],
+    outputs=gr.Textbox(label="Extracted Keywords", lines=10),
+    title="📑 Keyword Extraction",
+    description="Extract keywords using TF-IDF scoring",
+    examples=[
+        [EXAMPLES["Scientific Abstract"], 10, True, 1],
+        [EXAMPLES["News Article"], 5, True, 1],
+        [EXAMPLES["Technical Documentation"], 8, False, 1]
+    ]
+)
 
 demo.launch(share=True)