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Update app.py
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app.py
CHANGED
@@ -63,39 +63,39 @@ layer_agent_config_rec = {
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def stream_response(messages: Iterable[ResponseChunk]):
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layer_outputs = {}
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progress_bar = st.progress(0)
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total_steps = len(messages) # Estimate total messages for progress tracking
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current_step = 0
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for message in messages:
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current_step += 1
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progress_bar.progress(current_step / total_steps)
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if message['response_type'] == 'intermediate':
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layer = message['metadata']['layer']
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if layer not in layer_outputs:
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layer_outputs[layer] = []
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layer_outputs[layer].append(message['delta'])
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#
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with st.container():
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st.markdown(f"
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for output in layer_outputs[layer]:
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st.markdown(f"- {output}")
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else:
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#
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for layer, outputs in layer_outputs.items():
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st.markdown(f"### Layer {layer} Final Output")
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for output in outputs:
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st.
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layer_outputs = {} # Reset for next layers
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#
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yield message['delta']
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progress_bar.empty() # Clear progress bar once done
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def set_moa_agent(
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main_model: str = default_config['main_model'],
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def stream_response(messages: Iterable[ResponseChunk]):
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layer_outputs = {} # Store outputs per layer
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progress_bar = st.progress(0) # Initialize progress bar
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total_steps = len(messages) # Estimate total messages for progress tracking
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current_step = 0
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for message in messages:
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current_step += 1
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progress_bar.progress(current_step / total_steps) # Update progress bar
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if message['response_type'] == 'intermediate':
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layer = message['metadata']['layer'] # Identify the layer
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if layer not in layer_outputs:
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layer_outputs[layer] = [] # Initialize if layer not seen yet
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layer_outputs[layer].append(message['delta']) # Add intermediate delta
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# Render intermediate output in real-time
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with st.container(): # Create a dynamic container for rendering
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st.markdown(f"### Layer {layer} (In Progress)")
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for output in layer_outputs[layer]:
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st.markdown(f"- {output}") # Render each intermediate result
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else:
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# Final rendering of outputs after processing completes for this layer
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for layer, outputs in layer_outputs.items():
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st.markdown(f"### Layer {layer} Final Output")
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for output in outputs:
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st.markdown(f"- {output}")
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layer_outputs = {} # Reset layer outputs for the next iteration
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yield message['delta'] # Yield the final processed result
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progress_bar.empty() # Clear progress bar after all processing is complete
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def set_moa_agent(
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main_model: str = default_config['main_model'],
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