Create app.py

app.py

@@ -0,0 +1,146 @@
+import gradio as gr
+import json
+import re
+from typing import List, Dict
+
+def split_text_into_sentences(text: str) -> Dict:
+    """
+    Split text into sentences and return as JSON
+    
+    Args:
+        text (str): Input text paragraph
+        
+    Returns:
+        Dict: JSON response with sentences and metadata
+    """
+    if not text or not text.strip():
+        return {
+            "status": "error",
+            "message": "Empty input text",
+            "sentences": [],
+            "count": 0
+        }
+    
+    # Clean the text
+    text = text.strip()
+    
+    # Simple sentence splitting using regex
+    # This pattern looks for sentence endings followed by whitespace or end of string
+    sentence_pattern = r'(?<=[.!?])\s+(?=[A-Z])'
+    
+    # Split the text
+    sentences = re.split(sentence_pattern, text)
+    
+    # Clean up sentences (remove extra whitespace)
+    sentences = [sentence.strip() for sentence in sentences if sentence.strip()]
+    
+    # Create response
+    response = {
+        "status": "success",
+        "sentences": sentences,
+        "count": len(sentences),
+        "original_length": len(text),
+        "metadata": {
+            "avg_sentence_length": sum(len(s) for s in sentences) / len(sentences) if sentences else 0,
+            "longest_sentence": max(len(s) for s in sentences) if sentences else 0,
+            "shortest_sentence": min(len(s) for s in sentences) if sentences else 0
+        }
+    }
+    
+    return response
+
+def format_json_output(result: Dict) -> str:
+    """Format the result as pretty JSON string"""
+    return json.dumps(result, indent=2, ensure_ascii=False)
+
+# Create Gradio interface
+with gr.Blocks(title="Text to Sentences API") as demo:
+    gr.Markdown("# Text to Sentences Splitter API")
+    gr.Markdown("Enter a text paragraph and get it split into sentences with JSON output.")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                label="Input Text",
+                placeholder="Enter your text paragraph here...",
+                lines=5,
+                max_lines=10
+            )
+            submit_btn = gr.Button("Split into Sentences", variant="primary")
+            
+        with gr.Column():
+            output_json = gr.JSON(
+                label="JSON Output",
+                show_label=True
+            )
+            
+    # Example inputs
+    gr.Examples(
+        examples=[
+            ["Hello world! How are you today? I hope you're doing well. This is a test sentence."],
+            ["The quick brown fox jumps over the lazy dog. Machine learning is fascinating! Natural language processing involves many complex tasks. Text processing is an important skill."],
+            ["What is artificial intelligence? AI refers to computer systems that can perform tasks typically requiring human intelligence. These systems can learn, reason, and adapt to new situations."]
+        ],
+        inputs=input_text,
+        outputs=output_json,
+        fn=split_text_into_sentences,
+        cache_examples=True
+    )
+    
+    # Connect the interface
+    submit_btn.click(
+        fn=split_text_into_sentences,
+        inputs=input_text,
+        outputs=output_json
+    )
+    
+    # API documentation
+    gr.Markdown("""
+    ## API Usage
+    
+    This app provides both a web interface and API endpoints.
+    
+    ### Using the API programmatically:
+    
+    ```python
+    import requests
+    import json
+    
+    # Replace with your actual Hugging Face Space URL
+    url = "https://your-username-text-splitter.hf.space/api/predict"
+    
+    payload = {
+        "data": ["Your text paragraph here..."]
+    }
+    
+    response = requests.post(url, json=payload)
+    result = response.json()
+    print(json.dumps(result["data"][0], indent=2))
+    ```
+    
+    ### cURL example:
+    ```bash
+    curl -X POST https://your-username-text-splitter.hf.space/api/predict \
+         -H "Content-Type: application/json" \
+         -d '{"data": ["Hello world! How are you? This is a test."]}'
+    ```
+    
+    ### Response format:
+    ```json
+    {
+      "status": "success",
+      "sentences": ["Hello world!", "How are you?", "This is a test."],
+      "count": 3,
+      "original_length": 45,
+      "metadata": {
+        "avg_sentence_length": 15.0,
+        "longest_sentence": 17,
+        "shortest_sentence": 12
+      }
+    }
+    ```
+    """)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()