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| import gradio as gr | |
| import json | |
| import requests | |
| import os | |
| # Use the Hugging Face Inference API instead of loading the model | |
| API_URL = "https://api-inference.huggingface.co/models/numind/NuExtract-1.5" | |
| headers = {"Authorization": f"Bearer {os.environ.get('HF_TOKEN', '')}"} | |
| def test_function(template, text): | |
| print(f"Test function called with template: {template[:30]} and text: {text[:30]}") | |
| return "Button clicked successfully", "Function was called" | |
| def extract_info(template, text): | |
| try: | |
| # Format prompt according to NuExtract-1.5 requirements | |
| prompt = f"<|input|>\n### Template:\n{template}\n### Text:\n{text}\n\n<|output|>" | |
| print(f"Processing with prompt: {prompt[:100]}...") | |
| # Call API instead of using local model | |
| payload = { | |
| "inputs": prompt, | |
| "parameters": { | |
| "max_new_tokens": 1000, | |
| "do_sample": False | |
| } | |
| } | |
| print("Calling API...") | |
| response = requests.post(API_URL, headers=headers, json=payload) | |
| if response.status_code != 200: | |
| print(f"API error: {response.status_code}, {response.text}") | |
| return f"❌ API Error: {response.status_code}", response.text | |
| # Process result | |
| result = response.json() | |
| # Handle different response formats | |
| if isinstance(result, list) and len(result) > 0: | |
| result_text = result[0].get("generated_text", "") | |
| else: | |
| result_text = str(result) | |
| # Split at output marker if present | |
| if "<|output|>" in result_text: | |
| json_text = result_text.split("<|output|>")[1].strip() | |
| else: | |
| json_text = result_text | |
| # Try to parse as JSON | |
| print("Parsing JSON...") | |
| try: | |
| extracted = json.loads(json_text) | |
| formatted = json.dumps(extracted, indent=2) | |
| except json.JSONDecodeError: | |
| print(f"JSON parsing failed. Raw output: {json_text[:100]}...") | |
| return "❌ JSON parsing error", json_text | |
| return "✅ Success", formatted | |
| except Exception as e: | |
| print(f"Error in extraction: {str(e)}") | |
| return f"❌ Error: {str(e)}", "{}" | |
| # Create a simple interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# NuExtract-1.5 Extraction Tool") | |
| with gr.Row(): | |
| with gr.Column(): | |
| template = gr.Textbox( | |
| label="JSON Template", | |
| value='{"name": "", "email": ""}', | |
| lines=5 | |
| ) | |
| text = gr.Textbox( | |
| label="Text to Extract From", | |
| value="Contact: John Smith ([email protected])", | |
| lines=8 | |
| ) | |
| # Two buttons for testing | |
| test_btn = gr.Button("Test Click") | |
| extract_btn = gr.Button("Extract Information", variant="primary") | |
| with gr.Column(): | |
| status = gr.Textbox(label="Status") | |
| output = gr.Textbox(label="Output", lines=10) | |
| # Connect both buttons | |
| test_btn.click( | |
| fn=test_function, | |
| inputs=[template, text], | |
| outputs=[status, output] | |
| ) | |
| extract_btn.click( | |
| fn=extract_info, | |
| inputs=[template, text], | |
| outputs=[status, output] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |