app.py

import gradio as gr
import json
import torch
import os
from transformers import AutoModelForCausalLM, AutoTokenizer
import spaces

title = """        # 🙋🏻‍♂️Welcome to 🌟Tonic's 🌊 Osmosis Structure - Text to JSON Converter
"""
description = """       
        Convert unstructured text into well-formatted JSON using the Osmosis Structure 0.6B model.
        This model is specifically trained for structured data extraction and format conversion.
        
        ### ℹ️ About Osmosis Structure
        
        - **Model**: Osmosis Structure 0.6B parameters
        - **Architecture**: Qwen3 (specialized for structured data)
        - **Purpose**: Converting unstructured text to structured JSON format
        - **Optimizations**: Fine-tuned for data extraction and format conversion tasks
        
        The model automatically identifies key information in your text and organizes it into logical JSON structures.
        """
joinus = """
## Join us :
🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [![Join us on Discord](https://img.shields.io/discord/1109943800132010065?label=Discord&logo=discord&style=flat-square)](https://discord.gg/qdfnvSPcqP) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [MultiTonic](https://github.com/MultiTonic)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""

# Model configuration
MODEL_NAME = "osmosis-ai/Osmosis-Structure-0.6B"

# Global variables to store the model and tokenizer
model = None
tokenizer = None

def load_model():
    """Load the Osmosis Structure model and tokenizer with HF token for gated repos.
    
    This function initializes the global model and tokenizer variables by loading them from Hugging Face.
    It handles authentication using the HF_KEY environment variable and provides helpful error messages
    for common issues like authentication failures or model not found errors.
    
    Returns:
        bool: True if model and tokenizer were loaded successfully, False otherwise.
        
    Example:
        >>> success = load_model()
        >>> if success:
        ...     print("Model loaded successfully!")
        ... else:
        ...     print("Failed to load model")
    """
    global model, tokenizer
    
    try:
        print("Loading Osmosis Structure model...")
        
        # Get HF token from environment variables
        hf_token = os.environ.get("HF_KEY")
        if not hf_token:
            print("⚠️ Warning: HF_KEY not found in environment variables")
            print("Attempting to load without token...")
            hf_token = None
        else:
            print("✅ HF token found, accessing gated repository...")
        
        # Load tokenizer with token
        print("Loading tokenizer...")
        tokenizer = AutoTokenizer.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True,
            token=hf_token
            )
        
        print("Loading model...")
        # Load model with token
        model = AutoModelForCausalLM.from_pretrained(
            MODEL_NAME,
            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
            device_map="auto" if torch.cuda.is_available() else None,
            trust_remote_code=True,
            token=hf_token
            )
        
        print("✅ Osmosis Structure model loaded successfully!")
        return True
        
    except Exception as e:
        error_msg = f"❌ Error loading model: {e}"
        print(error_msg)
        
        # Provide helpful error messages for common issues
        if "401" in str(e) or "authentication" in str(e).lower():
            print("💡 This appears to be an authentication error.")
            print("Please ensure:")
            print("1. HF_KEY is set correctly in your Space secrets")
            print("2. Your token has access to the gated repository")
            print("3. You have accepted the model's license agreement")
        elif "404" in str(e) or "not found" in str(e).lower():
            print("💡 Model repository not found.")
            print("Please check if the model name is correct and accessible")
        
        return False

@spaces.GPU
def text_to_json(input_text, schema_text, max_tokens=512, temperature=0.6, top_p=0.95, top_k=20):
    """Convert plain text to structured JSON using Osmosis Structure model.
    
    This function takes unstructured text and optionally a JSON schema, then uses the Osmosis Structure
    model to convert it into well-formatted JSON. The output will follow the provided schema if one is
    given, otherwise it will create a logical structure based on the input text.
    
    Args:
        input_text (str): The unstructured text to convert to JSON.
        schema_text (str): Optional JSON schema that defines the desired output structure.
        max_tokens (int, optional): Maximum number of tokens to generate. Defaults to 512.
        temperature (float, optional): Controls randomness in generation. Defaults to 0.6.
        top_p (float, optional): Nucleus sampling parameter. Defaults to 0.95.
        top_k (int, optional): Number of highest probability tokens to consider. Defaults to 20.
    
    Returns:
        str: A JSON string containing the structured data, or an error message if something went wrong.
        
    Example:
        >>> input_text = "The conference will be held on June 10-12, 2024 at the Grand Hotel."
        >>> schema = '{"type": "object", "properties": {"event_start_date": {"type": "string", "format": "date"}}}'
        >>> result = text_to_json(input_text, schema)
        >>> print(result)
        {
          "event_start_date": "2024-06-10"
        }
    """
    global model, tokenizer
    
    if model is None or tokenizer is None:
        return "❌ Model not loaded. Please check the console for loading errors."
    
    try:
        # Create a structured prompt for JSON conversion
        system_prompt = "You are a helpful assistant that converts unstructured text into well-formatted JSON. Extract key information and organize it into a logical, structured format. Always respond with valid JSON."
        
        if schema_text and schema_text.strip():
            system_prompt = f"You are a helpful assistant that understands and translates text to JSON format according to the following schema. {schema_text}"
        
        messages = [
            {
                "role": "system",
                "content": system_prompt
            },
            {
                "role": "user", 
                "content": f"Convert this text to JSON format:\n\n{input_text}"
            }
        ]
        
        # Apply chat template
        formatted_prompt = tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        
        # Tokenize the input
        inputs = tokenizer(
            formatted_prompt,
            return_tensors="pt",
            truncation=True,
            max_length=2048
        )
        
        # Move to device if using GPU
        if torch.cuda.is_available():
            inputs = {k: v.to(model.device) for k, v in inputs.items()}
        
        # Generation parameters based on model config
        generation_config = {
            "max_new_tokens": max_tokens,
            "temperature": temperature,
            "top_p": top_p,
            "top_k": top_k,
            "do_sample": True,
            "pad_token_id": tokenizer.pad_token_id,
            "eos_token_id": tokenizer.eos_token_id,
            "repetition_penalty": 1.1,
        }
        
        # Generate response
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                **generation_config
            )
        
        # Decode the response
        generated_tokens = outputs[0][len(inputs["input_ids"][0]):]
        generated_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
        
        # Clean up the response
        generated_text = generated_text.strip()
        
        # Try to extract JSON from the response
        json_start = generated_text.find('{')
        json_end = generated_text.rfind('}')
        
        if json_start != -1 and json_end != -1 and json_end > json_start:
            json_text = generated_text[json_start:json_end+1]
        else:
            # If no clear JSON boundaries, try to clean the whole response
            json_text = generated_text
            
            # Remove common prefixes
            prefixes_to_remove = ["```json", "```", "Here's the JSON:", "JSON:", "```json\n"]
            for prefix in prefixes_to_remove:
                if json_text.startswith(prefix):
                    json_text = json_text[len(prefix):].strip()
            
            # Remove common suffixes
            suffixes_to_remove = ["```", "\n```"]
            for suffix in suffixes_to_remove:
                if json_text.endswith(suffix):
                    json_text = json_text[:-len(suffix)].strip()
        
        # Validate and format JSON
        try:
            parsed_json = json.loads(json_text)
            return json.dumps(parsed_json, indent=2, ensure_ascii=False)
        except json.JSONDecodeError:
            # If still not valid JSON, return the cleaned text with a note
            return f"Generated response (may need manual cleanup):\n\n{json_text}"
            
    except Exception as e:
        return f"❌ Error generating JSON: {str(e)}"

def create_demo():
    """Create and configure the Gradio demo interface.
    
    This function sets up the Gradio interface with all necessary components:
    - Input text area for unstructured text
    - Schema input area for JSON schema
    - Generation settings controls
    - Output display area
    - Example inputs with corresponding schemas
    
    Returns:
        gr.Blocks: A configured Gradio interface ready to be launched.
        
    Example:
        >>> demo = create_demo()
        >>> demo.launch()
    """
    # Fixed: Remove duplicate with gr.Blocks declaration
    with gr.Blocks(
        title=title,
        theme=gr.themes.Monochrome(),
        css="""
        .gradio-container {
            max-width: 1200px !important;
        }
        """
    ) as demo:
        # Header section
        # gr.Markdown(title)
        
        # Info section
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown(description)
            with gr.Column(scale=1):
                gr.Markdown(joinus)
        
        with gr.Row():
            with gr.Column(scale=1):
                input_text = gr.Textbox(
                    label="📝 Input Text",
                    placeholder="Enter your unstructured text here...\n\nExample: 'The conference will be held on June 10-12, 2024 at the Grand Hotel in San Francisco. Registration fee is $500 for early bird (before May 1) and $650 for regular registration. Contact [email protected] for questions.'",
                    lines=8,
                    max_lines=15
                )
                
                schema_text = gr.Textbox(
                    label="📋 JSON Schema (Optional)",
                    placeholder="Enter your JSON schema here...\n\nExample: {\"type\": \"object\", \"properties\": {\"event_start_date\": {\"type\": \"string\", \"format\": \"date\"}, \"event_end_date\": {\"type\": \"string\", \"format\": \"date\"}, \"location\": {\"type\": \"string\"}, \"registration_fees\": {\"type\": \"object\", \"properties\": {\"early_bird_price\": {\"type\": \"number\"}, \"regular_price\": {\"type\": \"number\"}, \"early_bird_deadline\": {\"type\": \"string\", \"format\": \"date\"}}}, \"contact_email\": {\"type\": \"string\"}}}",
                    lines=8,
                    max_lines=15
                )
                
                with gr.Accordion("⚙️ Generation Settings", open=False):
                    max_tokens = gr.Slider(
                        minimum=50,
                        maximum=1000,
                        value=512,
                        step=10,
                        label="Max Tokens",
                        info="Maximum number of tokens to generate"
                    )
                    
                    temperature = gr.Slider(
                        minimum=0.1,
                        maximum=1.0,
                        value=0.6,
                        step=0.1,
                        label="Temperature",
                        info="Controls randomness (lower = more focused)"
                    )
                    
                    top_p = gr.Slider(
                        minimum=0.1,
                        maximum=1.0,
                        value=0.95,
                        step=0.05,
                        label="Top-p",
                        info="Nucleus sampling parameter"
                    )
                    
                    top_k = gr.Slider(
                        minimum=1,
                        maximum=100,
                        value=20,
                        step=1,
                        label="Top-k",
                        info="Limits vocabulary for generation"
                    )
                
                convert_btn = gr.Button(
                    "🔄 Convert to JSON",
                    variant="primary",
                    size="lg"
                )
                
            with gr.Column(scale=1):
                output_json = gr.Textbox(
                    label="📋 Generated JSON",
                    lines=15,
                    max_lines=20,
                    interactive=False,
                    show_copy_button=True
                )
        
        # Examples section
        gr.Examples(
            examples=[
                [
                    "The conference will be held on June 10-12, 2024 at the Grand Hotel in San Francisco. Registration fee is $500 for early bird (before May 1) and $650 for regular registration. Contact [email protected] for questions.",
                    '{"type": "object", "properties": {"event_start_date": {"type": "string", "format": "date"}, "event_end_date": {"type": "string", "format": "date"}, "location": {"type": "string"}, "registration_fees": {"type": "object", "properties": {"early_bird_price": {"type": "number"}, "regular_price": {"type": "number"}, "early_bird_deadline": {"type": "string", "format": "date"}}}, "contact_email": {"type": "string"}}}'
                ],
                [
                    "The workshop is scheduled for March 15-16, 2024 at Tech Hub in Seattle. Early bird tickets cost $299 until February 15, after which regular tickets will be $399. For inquiries, email [email protected]",
                    '{"type": "object", "properties": {"event_start_date": {"type": "string", "format": "date"}, "event_end_date": {"type": "string", "format": "date"}, "location": {"type": "string"}, "registration_fees": {"type": "object", "properties": {"early_bird_price": {"type": "number"}, "regular_price": {"type": "number"}, "early_bird_deadline": {"type": "string", "format": "date"}}}, "contact_email": {"type": "string"}}}'
                ],
                [
                    "Product: Wireless Headphones Model XYZ-100. Price: $199.99. Features: Bluetooth 5.0, 30-hour battery, noise cancellation, wireless charging case. Colors available: Black, White, Blue. Warranty: 2 years. Rating: 4.5/5 stars (324 reviews).",
                    '{"type": "object", "properties": {"product_name": {"type": "string"}, "price": {"type": "number"}, "features": {"type": "array", "items": {"type": "string"}}, "colors": {"type": "array", "items": {"type": "string"}}, "warranty_years": {"type": "number"}, "rating": {"type": "object", "properties": {"score": {"type": "number"}, "reviews": {"type": "number"}}}}}'
                ],
                [
                    "The summer festival runs from July 1-5, 2024 at Central Park. VIP passes are $150 until June 1, then $200. General admission is $75 early bird (until June 15) and $100 regular. Contact [email protected]",
                    '{"type": "object", "properties": {"event_start_date": {"type": "string", "format": "date"}, "event_end_date": {"type": "string", "format": "date"}, "location": {"type": "string"}, "ticket_prices": {"type": "object", "properties": {"vip": {"type": "object", "properties": {"early_bird": {"type": "number"}, "regular": {"type": "number"}, "early_bird_deadline": {"type": "string", "format": "date"}}}, "general": {"type": "object", "properties": {"early_bird": {"type": "number"}, "regular": {"type": "number"}, "early_bird_deadline": {"type": "string", "format": "date"}}}}}, "contact_email": {"type": "string"}}}'
                ]
            ],
            inputs=[input_text, schema_text],
            label="Click on any example to try it"
        )
        

        # Event handlers
        convert_btn.click(
            fn=text_to_json,
            inputs=[input_text, schema_text, max_tokens, temperature, top_p, top_k],
            outputs=output_json,
            show_progress=True
        )
        
        # Allow Enter key to trigger conversion
        input_text.submit(
            fn=text_to_json,
            inputs=[input_text, schema_text, max_tokens, temperature, top_p, top_k],
            outputs=output_json,
            show_progress=True
        )
    
    return demo

# Initialize the demo
if __name__ == "__main__":
    print("🌊 Initializing Osmosis Structure Demo...")
    
    # Check HF token availability
    hf_token = os.environ.get("HF_KEY")
    if hf_token:
        print("✅ HF_KEY found in environment")
    else:
        print("⚠️ HF_KEY not found - this may cause issues with gated repositories")
    
    # Load model at startup
    if load_model():
        print("🚀 Creating Gradio interface...")
        demo = create_demo()
        demo.launch(
            ssr_mode=False,
            mcp_server=True
        )
    else:
        print("❌ Failed to load model. Please check your HF_KEY and model access permissions.")