How to Build Streamlit Research UI with Agents

import os, requests, json
from datetime import datetime

API_KEY = os.environ['SCAVIO_API_KEY']
SH = {'x-api-key': API_KEY, 'Content-Type': 'application/json'}

def research(topic, platforms=None):
    """Run multi-platform research on a topic."""
    if platforms is None:
        platforms = [None, 'reddit', 'youtube']
    all_results = []
    for platform in platforms:
        body = {'query': topic, 'country_code': 'us'}
        if platform:
            body['platform'] = platform
        data = requests.post('https://api.scavio.dev/api/v1/search',
            headers=SH, json=body).json()
        for r in data.get('organic_results', [])[:5]:
            all_results.append({
                'title': r.get('title', ''),
                'link': r.get('link', ''),
                'snippet': r.get('snippet', ''),
                'platform': platform or 'google',
                'position': r.get('position', 0)
            })
    return all_results

# Test the backend
results = research('ai agent frameworks 2026')
print(f'Research returned {len(results)} results across {len(set(r["platform"] for r in results))} platforms')
for r in results[:3]:
    print(f'  [{r["platform"]}] {r["title"][:50]}')

# streamlit_research.py
# Run with: streamlit run streamlit_research.py

# Note: This is the Streamlit app code. Testing the logic here without Streamlit.

def format_results_for_display(results):
    """Format results as the Streamlit app would display them."""
    by_platform = {}
    for r in results:
        p = r['platform']
        if p not in by_platform:
            by_platform[p] = []
        by_platform[p].append(r)
    output = []
    for platform, items in by_platform.items():
        output.append(f'\n## {platform.upper()} ({len(items)} results)')
        for item in items:
            output.append(f'  - [{item["title"][:50]}]({item["link"]})')
            output.append(f'    {item["snippet"][:80]}')
    return '\n'.join(output)

# Simulate Streamlit workflow
topic = 'best search api for ai agents'
results = research(topic)
formatted = format_results_for_display(results)
print(f'=== Research Results: "{topic}" ===')
print(formatted)
print(f'\nCost: ${len(set(r["platform"] for r in results)) * 0.005:.3f}')

TRUSTED_DOMAINS = ['github.com', 'stackoverflow.com', 'docs.python.org', 'arxiv.org',
                   'developer.mozilla.org', 'aws.amazon.com', 'cloud.google.com']
FORUM_DOMAINS = ['reddit.com', 'news.ycombinator.com', 'dev.to']

def score_source(result):
    link = result.get('link', '')
    score = 50  # base score
    for d in TRUSTED_DOMAINS:
        if d in link:
            score += 30
            break
    for d in FORUM_DOMAINS:
        if d in link:
            score += 10
            break
    if '2026' in result.get('title', '') or '2026' in result.get('snippet', ''):
        score += 15
    if result.get('position', 99) <= 3:
        score += 10
    return min(score, 100)

def research_with_scores(topic):
    results = research(topic)
    for r in results:
        r['quality_score'] = score_source(r)
    results.sort(key=lambda x: x['quality_score'], reverse=True)
    print(f'\n=== Scored Research: "{topic}" ===')
    for r in results[:8]:
        print(f'  [{r["quality_score"]:3}] [{r["platform"]:7}] {r["title"][:45]}')
    avg = sum(r['quality_score'] for r in results) / len(results) if results else 0
    print(f'\n  Avg quality: {avg:.0f}/100 | Sources: {len(results)}')

research_with_scores('python web framework comparison 2026')