Spaces:
Sleeping
Sleeping
File size: 10,766 Bytes
f3950bb d5b9d5e f3950bb a570d68 f3950bb d5b9d5e a570d68 f3950bb a570d68 f3950bb a570d68 f3950bb a570d68 f3950bb a570d68 f3950bb d5b9d5e f3950bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
"""
Frontend Gradio App for RAM-P (PUBLIC)
This is the public-facing UI that communicates with the private backend via API.
Deploy this as a PUBLIC Hugging Face Space.
"""
import gradio as gr
import os
from gradio_client import Client
# Get backend URL and token from environment variables
BACKEND_URL = os.getenv("BACKEND_URL", "") # e.g., "https://username-backend.hf.space"
HF_TOKEN = os.getenv("HF_TOKEN", "") # Hugging Face token for authentication
if not BACKEND_URL:
raise ValueError("BACKEND_URL environment variable must be set!")
if not HF_TOKEN:
raise ValueError("HF_TOKEN environment variable must be set!")
# Initialize backend client
try:
# For private Spaces, pass token via headers or use HF_TOKEN environment variable
# Gradio Client automatically uses HF_TOKEN env var if available
if HF_TOKEN:
# Set as environment variable for gradio-client to pick up
os.environ["HF_TOKEN"] = HF_TOKEN
backend_client = Client(BACKEND_URL)
else:
backend_client = Client(BACKEND_URL)
print(f"Connected to backend at {BACKEND_URL}")
# Debug: List available API endpoints
try:
api_info = backend_client.view_api()
print(f"Available API endpoints: {list(api_info.keys()) if isinstance(api_info, dict) else 'Could not list APIs'}")
except:
pass
except Exception as e:
print(f"Warning: Could not connect to backend: {e}")
print(f"Make sure BACKEND_URL and HF_TOKEN are set correctly.")
print(f"BACKEND_URL: {BACKEND_URL}")
print(f"HF_TOKEN set: {bool(HF_TOKEN)}")
backend_client = None
def add_sentences_ui(sentences_text):
"""UI handler for adding sentences."""
if not backend_client:
return "❌ Backend not available. Please check configuration.", "**Error:** Backend connection failed."
try:
result = backend_client.predict(
sentences_text,
api_name="/api_add_sentences"
)
if isinstance(result, dict):
vocab_info = result.get("vocab_info", {})
vocab_text = f"**Current Vocabulary:** {vocab_info.get('vocab_size', 0)} words\n**Corpus:** {vocab_info.get('corpus_size', 0)} sentences\n**Trained:** {vocab_info.get('trained_size', 0)} sentences"
return result.get("status", "Unknown status"), vocab_text
else:
return str(result), "**Error:** Unexpected response format."
except Exception as e:
return f"❌ Error: {str(e)}", "**Error:** Could not connect to backend."
def train_brain_ui(epochs, progress=gr.Progress()):
"""UI handler for training."""
if not backend_client:
yield "❌ Backend not available. Please check configuration."
return
try:
yield "🔄 Training in progress... Please wait..."
result = backend_client.predict(
int(epochs),
api_name="/api_train"
)
if isinstance(result, str):
yield result
elif isinstance(result, dict):
yield result.get("status", "Training completed.")
else:
yield str(result)
except Exception as e:
yield f"❌ Error: {str(e)}"
def run_stream_ui(seed_word, steps, coupling_gain, transmission_threshold):
"""UI handler for stream simulation."""
if not backend_client:
return None, "❌ Backend not available. Please check configuration."
try:
result = backend_client.predict(
seed_word,
int(steps),
float(coupling_gain),
float(transmission_threshold),
api_name="/api_run_stream"
)
# Gradio returns tuple/list for multiple outputs
if isinstance(result, (list, tuple)) and len(result) >= 2:
return result[0], result[1]
elif isinstance(result, dict):
return result.get("image"), result.get("text", "")
else:
return None, f"Unexpected response: {result}"
except Exception as e:
return None, f"❌ Error: {str(e)}"
def clear_brain_ui():
"""UI handler for clearing the brain."""
if not backend_client:
return "❌ Backend not available. Please check configuration.", "**Error:** Backend connection failed."
try:
result = backend_client.predict(api_name="/api_clear_brain")
if isinstance(result, dict):
vocab_info = result.get("vocab_info", {})
vocab_text = f"**Current Vocabulary:** {vocab_info.get('vocab_size', 0)} words\n**Corpus:** {vocab_info.get('corpus_size', 0)} sentences\n**Trained:** {vocab_info.get('trained_size', 0)} sentences"
return result.get("status", "Cleared."), vocab_text
else:
return str(result), "**Error:** Unexpected response format."
except Exception as e:
return f"❌ Error: {str(e)}", "**Error:** Could not connect to backend."
# Create Frontend Interface
with gr.Blocks(title="RAM-P - Interactive Learning") as frontend_app:
gr.Markdown("""
# 🧠 RAM-P - Interactive Learning
**Start with a blank brain and teach it by adding sentences!**
### How to use:
1. **Add Sentences**: Input sentences (one per line) to build vocabulary and corpus
2. **Train Brain**: Click "Train Brain" to let it learn associations from your sentences
3. **Run Stream**: Enter a seed word and watch the stream of consciousness flow!
""")
with gr.Tabs():
with gr.Tab("1. Add Sentences"):
gr.Markdown("### Add sentences to teach the brain")
gr.Markdown("Enter sentences (one per line). The brain will extract vocabulary from these sentences.")
sentences_input = gr.Textbox(
label="Sentences",
placeholder="the monkey ate a banana\nprogrammer wrote code\nastronomer saw stars",
lines=10,
info="Enter sentences, one per line"
)
add_btn = gr.Button("Add Sentences", variant="primary")
add_output = gr.Textbox(label="Status", interactive=False)
vocab_display = gr.Markdown(label="Vocabulary Info")
add_btn.click(
fn=add_sentences_ui,
inputs=sentences_input,
outputs=[add_output, vocab_display]
)
with gr.Tab("2. Train Brain"):
gr.Markdown("### Train the brain on your corpus")
gr.Markdown("The brain will learn associations between words that appear together in sentences. **Incremental learning**: Adding new sentences expands the brain without losing previous knowledge.")
with gr.Row():
with gr.Column(scale=2):
epochs_slider = gr.Slider(
label="Training Epochs",
minimum=1,
maximum=10,
value=2,
step=1,
info="Number of times to go through the corpus"
)
train_btn = gr.Button("Train Brain", variant="primary", size="lg")
train_output = gr.Markdown(label="Training Status", value="Ready to train. Click 'Train Brain' to start.")
train_btn.click(
fn=train_brain_ui,
inputs=epochs_slider,
outputs=train_output
)
with gr.Column(scale=1):
gr.Markdown("### Brain Management")
clear_btn = gr.Button("Clear Brain", variant="stop", size="lg")
clear_output = gr.Markdown(label="Clear Status")
clear_vocab_display = gr.Markdown(label="Vocabulary Info")
clear_btn.click(
fn=clear_brain_ui,
inputs=None,
outputs=[clear_output, clear_vocab_display]
)
with gr.Tab("3. Stream of Consciousness"):
gr.Markdown("### Run stream of consciousness simulation")
gr.Markdown("Enter a seed word and watch how the brain's thoughts flow and associate.")
with gr.Row():
with gr.Column(scale=1):
seed_word_input = gr.Textbox(
label="Seed Word",
value="",
placeholder="Enter a word from your vocabulary...",
info="The initial concept to inject"
)
steps_slider = gr.Slider(
label="Simulation Steps",
minimum=100,
maximum=1000,
value=400,
step=50
)
coupling_slider = gr.Slider(
label="Coupling Gain",
minimum=0.0,
maximum=200.0,
value=80.0,
step=5.0,
info="How strongly thoughts pull on each other"
)
threshold_slider = gr.Slider(
label="Transmission Threshold",
minimum=0.01,
maximum=0.5,
value=0.05,
step=0.01,
info="Minimum activation for influence"
)
stream_btn = gr.Button("Run Stream", variant="primary", size="lg")
with gr.Column(scale=2):
stream_image = gr.Image(
label="Stream of Consciousness Visualization",
type="filepath"
)
stream_text = gr.Markdown(label="Narrative Chain")
stream_btn.click(
fn=run_stream_ui,
inputs=[seed_word_input, steps_slider, coupling_slider, threshold_slider],
outputs=[stream_image, stream_text]
)
gr.Markdown("""
---
**Tips:**
- Add diverse sentences to build a rich vocabulary
- More training epochs = stronger associations
- Try different seed words to see different thought patterns
""")
if __name__ == "__main__":
# Let Gradio use default port (7860) or GRADIO_SERVER_PORT env var
# Don't hardcode port - let Hugging Face Spaces handle it
frontend_app.launch()
|