diff --git a/requirements.txt b/requirements.txt index d8852f3..7e78062 100644 --- a/requirements.txt +++ b/requirements.txt @@ -12,3 +12,6 @@ Pillow>=10.0.0 # Logo display in GUI gspread>=5.10.0 # Google Sheets API google-auth>=2.22.0 # Google authentication rich>=13.0.0 + +# Web UI +flask>=3.0.0 diff --git a/templates/index.html b/templates/index.html new file mode 100644 index 0000000..62065fe --- /dev/null +++ b/templates/index.html @@ -0,0 +1,645 @@ + + + + + +News Intelligence System + + + +
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News Intelligence System

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Multi-AI Article Scoring — 5 models, 1 consensus

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News SourceFetch articles
+ → +
Health CheckVerify APIs
+ → +
AI Scoring5 models
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Peer ReviewCross-check
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Fact CheckPerplexity
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ConsensusFinal score
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+ + +
Select an Article to Analyze
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Starting analysis...
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AI Models
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ChatGPT
Idle
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Claude
Idle
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Gemini
Idle
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Grok
Idle
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Perplexity
Idle
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--
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AI Radar Score (out of 10)
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+ Confidence: -- + Variance: -- + Action: -- +
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Individual Scores

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Peer Review

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AI 1ScoreAI 2ScoreResult
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Fact Check

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Key Insights

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AI Rationales

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+ + + + diff --git a/web_app.py b/web_app.py new file mode 100644 index 0000000..a8e12fa --- /dev/null +++ b/web_app.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +""" +News Intelligence System - Web UI +Flask-based web interface for multi-AI article scoring. + +Run: python web_app.py +Then open http://localhost:5000 +""" + +import json +import os +import queue +import threading +import time + +from flask import Flask, Response, jsonify, render_template, request + +# Reuse data from demo.py +from demo import AI_MODELS, SAMPLE_ARTICLES, SIMULATED_SCORES + +app = Flask(__name__) + +# Global queue for SSE events per analysis session +analysis_events = {} + + +def _send_event(session_id, event_type, data): + """Push an SSE event to the session queue.""" + if session_id in analysis_events: + analysis_events[session_id].put({"event": event_type, "data": data}) + + +def run_demo_analysis(session_id, article_index): + """Run a simulated analysis pipeline with progress events.""" + article = SAMPLE_ARTICLES[article_index] + _send_event(session_id, "step", {"step": "health_check", "message": "Checking AI engines..."}) + + # Step 0: Health check + for model, config in AI_MODELS.items(): + time.sleep(0.3) + _send_event( + session_id, + "health", + { + "model": model, + "status": "online", + "latency": 42 + hash(model) % 50, + }, + ) + _send_event( + session_id, "step", {"step": "health_check_done", "message": "All 5 AI engines operational"} + ) + time.sleep(0.3) + + # Step 1: Individual scoring + _send_event(session_id, "step", {"step": "scoring", "message": "AI models scoring article..."}) + for model, data in SIMULATED_SCORES.items(): + time.sleep(0.5) + _send_event( + session_id, + "score", + { + "model": model, + "score": data["score"], + "reasoning": data["reasoning"], + "strengths": data["strengths"], + "concerns": data["concerns"], + }, + ) + time.sleep(0.3) + + # Step 2: Peer review + _send_event( + session_id, "step", {"step": "peer_review", "message": "Peer review in progress..."} + ) + pairs = [ + ("ChatGPT", "Perplexity", "AGREEMENT"), + ("Claude", "Grok", "AGREEMENT"), + ("Gemini", "Claude", "DISAGREEMENT"), + ] + for ai1, ai2, result in pairs: + time.sleep(0.4) + _send_event( + session_id, + "peer", + { + "ai1": ai1, + "ai2": ai2, + "score1": SIMULATED_SCORES[ai1]["score"], + "score2": SIMULATED_SCORES[ai2]["score"], + "result": result, + }, + ) + time.sleep(0.3) + + # Step 3: Fact check + _send_event( + session_id, "step", {"step": "fact_check", "message": "Perplexity fact-checking claims..."} + ) + time.sleep(0.5) + fact_checks = [ + { + "claim": "15-20% improvement on reasoning tests", + "verdict": "VERIFIED", + "details": "Matches published benchmarks", + }, + { + "claim": "Human-level reasoning", + "verdict": "PARTIAL", + "details": "True for narrow tasks, contested for general", + }, + ] + _send_event(session_id, "facts", {"checks": fact_checks}) + time.sleep(0.3) + + # Step 4: Consensus + _send_event( + session_id, "step", {"step": "consensus", "message": "Calculating consensus score..."} + ) + time.sleep(0.5) + scores = [d["score"] for d in SIMULATED_SCORES.values()] + avg_score = sum(scores) / len(scores) + _send_event( + session_id, + "consensus", + { + "score": round(avg_score, 1), + "confidence": "HIGH", + "variance": 0.8, + "recommendation": "SHARE WITH CONTEXT", + }, + ) + time.sleep(0.3) + + # Step 5: Insights + _send_event(session_id, "step", {"step": "insights", "message": "Generating insights..."}) + time.sleep(0.3) + insights = [ + { + "category": "FACTUAL ACCURACY", + "rating": "High", + "details": "Claims verified against published sources", + }, + { + "category": "SOURCE QUALITY", + "rating": "Excellent", + "details": f"{article['source']} is Tier-1 publication", + }, + { + "category": "HEADLINE CONCERN", + "rating": "Moderate", + "details": "'Human-level' framing debated", + }, + { + "category": "RECOMMENDATION", + "rating": "Share with context", + "details": "Good for professional network", + }, + ] + _send_event(session_id, "insights", {"insights": insights}) + + # Done + _send_event(session_id, "step", {"step": "done", "message": "Analysis complete"}) + _send_event(session_id, "done", {}) + + +@app.route("/") +def index(): + return render_template("index.html") + + +@app.route("/api/articles") +def get_articles(): + return jsonify(SAMPLE_ARTICLES) + + +@app.route("/api/analyze", methods=["POST"]) +def start_analysis(): + data = request.get_json() + article_index = data.get("article_index", 0) + session_id = f"session_{int(time.time() * 1000)}" + analysis_events[session_id] = queue.Queue() + + thread = threading.Thread( + target=run_demo_analysis, args=(session_id, article_index), daemon=True + ) + thread.start() + + return jsonify({"session_id": session_id}) + + +@app.route("/api/analyze/stream") +def stream_analysis(): + session_id = request.args.get("session_id") + if not session_id or session_id not in analysis_events: + return Response("Invalid session", status=400) + + def generate(): + q = analysis_events[session_id] + while True: + try: + event = q.get(timeout=30) + yield f"event: {event['event']}\ndata: {json.dumps(event['data'])}\n\n" + if event["event"] == "done": + break + except queue.Empty: + yield "event: ping\ndata: {}\n\n" + # Cleanup + analysis_events.pop(session_id, None) + + return Response( + generate(), + mimetype="text/event-stream", + headers={ + "Cache-Control": "no-cache", + "X-Accel-Buffering": "no", + }, + ) + + +@app.route("/api/sample-report") +def sample_report(): + report_path = os.path.join(os.path.dirname(__file__), "sample_output", "analysis_report.json") + with open(report_path) as f: + return jsonify(json.load(f)) + + +if __name__ == "__main__": + app.run(debug=os.environ.get("FLASK_DEBUG", "0") == "1", host="0.0.0.0", port=5000)