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from gradio import Interface, Textbox, Markdown, Slider, Chatbot, ChatMessage, Blocks, Row, Column, ClearButton, Button, Number,Label
from final_graph import create_final_graph, render_bettafish_report
from forum_graph import create_forum_graph
from graph_states import FinalState, ForumState
from langchain_core.callbacks import UsageMetadataCallbackHandler
import uuid
import time
graph = None
async def init_graph():
global graph
if graph is None:
graph = await create_final_graph()
async def process_inputs(query, refinements, max_rounds):
state = FinalState(
query=query,
vector_store="",
query_limit=int(refinements),
max_rounds=int(max_rounds),
current_round=1,
step=0,
round_digests=[],
running_mermaid_graph="",
debate_history=[],
final_report=None,
messages = [],
references = []
)
await init_graph()
messages = []
thread_id = str(uuid.uuid4())
usage_callback = UsageMetadataCallbackHandler()
config = {"configurable":{"thread_id":thread_id},"recursion_limit":100,"callbacks":[usage_callback]}
step = 0
current_round = 1
current_round_md = "### Current Round: {current_round}"
start_time = time.time()
token_used = 0
input_tokens = 0
output_tokens = 0
role = "user"
status = "Searching the web for relevant information..."
try:
async for parent,child in graph.astream(state,config=config,stream_mode="updates",subgraphs=True):
print("Parent:",parent)
print("Child:",child,end="\n\n")
if not parent or len(parent)==0:
continue
parent_node = parent[0].split(":")[0]
curr_node = list(child.keys())[0]
values = child.get(curr_node,{})
if parent_node == "Research":
if curr_node == "Query Refiner":
status = "Searching with refined queries..."
elif parent_node == "Forum Graph":
if curr_node == "Debate Supervisor":
step = values.get("step",step)
msgs = values.get("messages",[])
if step%2 == 0:
role = "assistant"
else:
role = "user"
if len(msgs)>0:
messages.append(ChatMessage(role=role,content=msgs[-1].content,metadata={"title":"Moderator Instructions"}))
status = "Analyzing and creating arguments..."
elif curr_node == "Persona Agent":
msgs = values.get("messages",[])
if msgs:
tool_call = False
curr_message = msgs[-1]
if len(curr_message.content.strip()) == 0 and len(curr_message.tool_calls)>0:
tool_call = True
if not tool_call:
messages.append(ChatMessage(role=role,content=curr_message.content))
else:
messages.append(ChatMessage(role=role,content="Tool Call Made. Awaiting Response...",metadata={"title":"Fetching researched data"}))
status = "Engaging in debate..."
elif curr_node == "Round Digest":
current_round += 1
status = "Preparing for next round..."
for model, usage_data in usage_callback.usage_metadata.items():
input_tokens += usage_data.get("input_tokens",0)
output_tokens += usage_data.get("output_tokens",0)
token_used += usage_data.get("total_tokens",0)
total_time_taken = round((time.time()-start_time),2)
yield messages, "" , status, current_round_md.format(current_round=current_round),total_time_taken,input_tokens,output_tokens, token_used
except Exception as e:
print("Error during running the graph:",e)
yield messages, "" , f" An error occurred during the graph execution: {e} ", current_round_md.format(current_round=current_round),total_time_taken,input_tokens,output_tokens, token_used
return
try:
snapshot = graph.get_state(config={"configurable":{"thread_id":thread_id}})
if snapshot.values:
report=render_bettafish_report(snapshot.values)
else:
report = "No state found. Did the graph finish?"
yield messages, report , " Graph Execution Completed! Report Generated Below. ", current_round_md.format(current_round=current_round),total_time_taken,input_tokens,output_tokens, token_used
except Exception as e:
print("Error during report generation:",e)
yield messages, "" , f" An error occurred during report generation: {e} ", current_round_md.format(current_round=current_round),total_time_taken,input_tokens,output_tokens, token_used
return
with Blocks() as demo:
Markdown("<div style='text-align:center;'> <h1> Strategic Debate Assistant </h1> </div>")
with Row():
with Column():
with Row():
refinements = Slider(0, 3, label="Max Query Refinements", step=1, value=0)
max_rounds = Slider(1, 10, label="Max Debate Rounds",step=1, value=1)
query = Textbox(label="Enter your strategic question here", lines=2)
with Row():
ClearButton([query,refinements,max_rounds], value="Reset")
submit_btn = Button("Submit",variant="primary")
with Row():
input_tokens = Number(label=" Input Tokens Used", value=0,interactive=False)
output_tokens = Number(label="Output Tokens Used", value=0,interactive=False)
total_tokens = Number(label="Total Tokens Used", value=0,interactive=False)
total_time = Number(label="Total Time Taken (s)", value=0,interactive=False)
label = Textbox(value=" Report will be generated below upon submission", label="Status", interactive=False )
with Column():
current_round = Markdown(label="Current round")
chatbot = Chatbot(label="Debate Conversation")
report_md = Markdown(label="Strategic Report")
submit_btn.click(process_inputs, inputs=[query, refinements, max_rounds], outputs=[chatbot, report_md,label, current_round, total_time,input_tokens,output_tokens,total_tokens])
demo.launch() |