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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()
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