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e4e2691 34d9cf1 e4e2691 ec47327 | 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 | from flask import Flask, render_template, request, Response, stream_with_context
import json
import time
from workflow_orchestrator import WorkflowOrchestrator
app = Flask(__name__)
# ==================== CONFIGURATION ====================
import os
COHERE_API_KEY = os.getenv("COHERE_API_KEY")
# ==================== FLASK ROUTES ====================
@app.route('/')
def index():
return render_template('index.html')
@app.route('/stream_workflow')
def stream_workflow():
task = request.args.get('task', 'Search about KSA Vision 2030')
def generate():
orchestrator = WorkflowOrchestrator(COHERE_API_KEY)
def on_event(event_type, payload):
data = {
'type': event_type,
}
if isinstance(payload, dict):
data.update(payload)
# 1. Start
yield f"data: {json.dumps({'type': 'status', 'node': 'start', 'msg': 'Connecting to Neural Network...'})}\n\n"
yield f"data: {json.dumps({'type': 'activate', 'node': 'planner', 'msg': 'Planner: Analyzing complexity...'})}\n\n"
plan = orchestrator.agents["planner"].execute(task, {})
steps = plan['steps']
yield f"data: {json.dumps({'type': 'log', 'msg': f'Strategized {len(steps)} execution steps based on real analysis.', 'role': 'planner'})}\n\n"
accumulated_data = ""
# 2. Execution Loop
for i, step in enumerate(steps):
yield f"data: {json.dumps({'type': 'activate', 'node': 'executor', 'msg': f'Researching: {step}'})}\n\n"
exec_res = orchestrator.agents["executor"].execute(step, {})
accumulated_data += f"\nSection {i+1}: {step}\n{exec_res['output']}\n"
yield f"data: {json.dumps({'type': 'activate', 'node': 'validator', 'msg': 'Verifying sources...'})}\n\n"
val_res = orchestrator.agents["validator"].execute(exec_res, {})
yield f"data: {json.dumps({'type': 'activate', 'node': 'decision', 'msg': 'Quality Gate'})}\n\n"
if val_res['is_valid']:
citations_count = len(exec_res.get('citations', []))
yield f"data: {json.dumps({'type': 'log', 'msg': f'✅ Validated with {citations_count} citations.', 'role': 'success'})}\n\n"
else:
yield f"data: {json.dumps({'type': 'log', 'msg': f'⚠️ Low confidence data.', 'role': 'warning'})}\n\n"
time.sleep(0.5)
# 3. Final Report
yield f"data: {json.dumps({'type': 'activate', 'node': 'end', 'msg': 'Generating Final Report...'})}\n\n"
final_prompt = f"""
You are an AI analyst. The user asked: "{task}".
Based on the following research data, write a comprehensive executive summary in Markdown:
{accumulated_data}
"""
final_report = orchestrator.co.chat(message=final_prompt, model="command-a-03-2025", temperature=0.3).text
yield f"data: {json.dumps({'type': 'finish', 'report': final_report})}\n\n"
return Response(stream_with_context(generate()), mimetype='text/event-stream')
if __name__ == "__main__":
app.run(host="0.0.0.0", port=7860) |