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| import gradio as gr | |
| import torch | |
| from load_model import load_model_and_tokenizer | |
| # --- 1. Load Model and Tokenizer --- | |
| # This is done once when the Gradio app starts. | |
| try: | |
| print("Loading model and tokenizer...") | |
| model, tokenizer = load_model_and_tokenizer() | |
| print("✅ Model and tokenizer loaded successfully.") | |
| except Exception as e: | |
| print(f"❌ Failed to load model: {e}") | |
| model, tokenizer = None, None | |
| # --- 2. Define the Prediction Function --- | |
| # This function is called every time a user interacts with the demo. | |
| def predict_futures(text): | |
| """ | |
| Takes raw text input, tokenizes it, gets model predictions, | |
| and formats the output for the Gradio interface. | |
| """ | |
| if not model or not tokenizer: | |
| return "Model not loaded. Please check the logs.", {} | |
| try: | |
| # a. Preprocess: Tokenize the input text | |
| token_ids = tokenizer.encode(text) | |
| tokens_tensor = torch.LongTensor(token_ids).unsqueeze(0) # Add batch dimension | |
| # b. Predict: Get model's raw output (logits) | |
| with torch.no_grad(): | |
| axis_logits, _, _ = model(tokens_tensor) | |
| # c. Post-process: Apply sigmoid to get probabilities (0-1) | |
| axis_predictions = torch.sigmoid(axis_logits) | |
| # d. Format Output: Create a dictionary for the label component | |
| axis_names = [ | |
| "Hyper-Automation", "Human-Tech Symbiosis", "Abundance", "Individualism", | |
| "Community Focus", "Global Interconnectedness", "Crisis & Collapse", "Restoration & Healing", | |
| "Adaptation & Resilience", "Digital Dominance", "Physical Embodiment", "Collaboration" | |
| ] | |
| # Create a dictionary of {label: confidence} | |
| confidences = {name: float(weight) for name, weight in zip(axis_names, axis_predictions[0])} | |
| # You can return a simple message and the formatted labels | |
| return "Prediction complete.", confidences | |
| except Exception as e: | |
| print(f"Error during prediction: {e}") | |
| return f"An error occurred: {e}", {} | |
| # --- 3. Create and Launch the Gradio Interface --- | |
| print("Creating Gradio interface...") | |
| # Define the input and output components | |
| input_text = gr.Textbox( | |
| lines=5, | |
| label="Input Scenario", | |
| placeholder="Describe a future scenario here..." | |
| ) | |
| output_text = gr.Textbox(label="Status") | |
| output_labels = gr.Label(label="Predicted Axis Weights", num_top_classes=12) | |
| # Build the interface | |
| demo = gr.Interface( | |
| fn=predict_futures, | |
| inputs=input_text, | |
| outputs=[output_text, output_labels], | |
| title="Futures Prediction Model", | |
| description=( | |
| "Explore multi-dimensional futures. " | |
| "Write a text describing a potential future scenario and see how the model scores it " | |
| "across 12 different axes, from 'Hyper-Automation' to 'Crisis & Collapse'." | |
| ), | |
| examples=[ | |
| ["In a future dominated by hyper-automation, societal structures adapt to new forms of labor and community."], | |
| ["Coastal cities adopt divergent strategies as sea levels rise. Singapore invests in autonomous seawall monitoring, while Jakarta facilitates managed retreat."], | |
| ["A global pandemic leads to a surge in community-focused initiatives and a renewed appreciation for local supply chains."] | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| print("Launching Gradio demo...") | |
| # The launch() command creates a shareable link to the demo. | |
| demo.launch() | |