Found {len(studies)} studies
'
if ranking_terms and ranking_terms.strip() and llm_model_name:
html_results += f'
🤖 Results ranked by {llm_model_name}
Ranking terms: "{ranking_terms}"
'
html_results += f'
Ranking applied: Studies have been reordered by AI relevance to "{ranking_terms}". The model analyzed each study\'s title, summary, conditions, and inclusion criteria to determine how closely they match your ranking terms. Higher-ranked studies appear first.
'
elif ranking_terms and ranking_terms.strip() and not llm_model_name:
# Check what's wrong
use_hf_api = os.environ.get('USE_HF_API', 'false').lower() == 'true'
hf_token = os.environ.get('HUGGINGFACE_API_TOKEN', '')
error_msg = "LLM service is not available."
if not use_hf_api:
error_msg += " Set USE_HF_API=true in your Space secrets."
if not hf_token:
error_msg += " Set HUGGINGFACE_API_TOKEN in your Space secrets."
html_results += f'
⚠️ Ranking not applied: {error_msg}
Check your Space Settings → Secrets to add the required environment variables.
'
for idx, study in enumerate(studies):
status_badge_color = {
'RECRUITING': '#28a745',
'NOT_YET_RECRUITING': '#ffc107',
'ACTIVE_NOT_RECRUITING': '#17a2b8',
'COMPLETED': '#6c757d',
'SUSPENDED': '#dc3545',
'TERMINATED': '#dc3545',
'WITHDRAWN': '#6c757d'
}.get(study['status'], '#6c757d')
# Add relevance score indicator if ranking was applied
relevance_indicator = ""
if 'relevance_score' in study:
score = study.get('relevance_score', 0)
score_percent = int(score * 100)
# Color based on score: green for high, yellow for medium, gray for low
if score >= 0.7:
score_color = '#28a745'
elif score >= 0.4:
score_color = '#ffc107'
else:
score_color = '#6c757d'
relevance_indicator = f'
Relevance: {score_percent}%'
match_terms = study.get("ranking_match_terms", [])
title_html = _highlight_html(study.get("title", ""), match_terms)
html_results += f"""
{title_html}
{html.escape(study['status'])}
{relevance_indicator}
NCT ID: {html.escape(study['nctId'])}
"""
if study.get('sponsor'):
html_results += f'
Sponsor: {html.escape(study["sponsor"])}
'
if study.get('conditions'):
conditions_html = ", ".join(_highlight_html(cond, match_terms) for cond in study["conditions"])
html_results += f'
Conditions: {conditions_html}
'
if study.get('briefSummary'):
summary = study['briefSummary'][:600] + '...' if len(study['briefSummary']) > 600 else study['briefSummary']
html_results += f'
Summary: {_highlight_html(summary, match_terms)}
'
if match_terms:
html_results += "
Why matched: "
html_results += "".join(
f"
{html.escape(term)}"
for term in match_terms[:10]
)
field_matches = study.get("ranking_match_fields", {})
if field_matches:
html_results += "
"
for field, terms in field_matches.items():
term_text = ", ".join(html.escape(t) for t in terms[:6])
html_results += f"
{html.escape(field)}: {term_text}
"
html_results += "
"
html_results += "
"
if study.get('inclusionCriteria'):
html_results += "
Inclusion Criteria ▼
"
for criterion in study['inclusionCriteria']:
html_results += f"- {_highlight_html(criterion, match_terms)}
"
html_results += "
"
if study.get('exclusionCriteria'):
html_results += "
Exclusion Criteria ▼
"
for criterion in study['exclusionCriteria']:
html_results += f"- {_highlight_html(criterion, match_terms)}
"
html_results += "
"
if study.get('ranking_reasoning'):
html_results += f"
Ranking Reasoning: {html.escape(study['ranking_reasoning'])}
"
html_results += f"""
"""
html_results += "