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Running on Zero
Running on Zero
hardiksood21
fix: add src directory to sys.path in app.py and app/app.py for Hugging Face Spaces
bb1269d | import os | |
| import sys | |
| # Add src to python path | |
| sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "src"))) | |
| import streamlit as st | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| from variantllm.inference.scorer import ZeroShotVariantScorer | |
| st.set_page_config( | |
| page_title="VariantLLM | Clinical Variant Effect Prediction Engine", | |
| page_icon="🧬", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Custom Styling | |
| st.markdown(""" | |
| <style> | |
| .main-title { font-size: 2.2rem; font-weight: 700; color: #1e293b; margin-bottom: 0.2rem; } | |
| .sub-title { font-size: 1.05rem; color: #64748b; margin-bottom: 1.5rem; } | |
| .metric-card { background: #f8fafc; padding: 1.2rem; border-radius: 10px; border: 1px solid #e2e8f0; } | |
| .badge-path { background: #fee2e2; color: #991b1b; padding: 4px 10px; border-radius: 6px; font-weight: 600; } | |
| .badge-benign { background: #dcfce7; color: #166534; padding: 4px 10px; border-radius: 6px; font-weight: 600; } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def load_scorer(): | |
| return ZeroShotVariantScorer(model_name="facebook/esm2_t6_8M_UR50D") | |
| scorer = load_scorer() | |
| st.markdown('<div class="main-title">🧬 VariantLLM: Clinical Variant Effect Prediction Engine</div>', unsafe_allow_html=True) | |
| st.markdown('<div class="sub-title">Zero-shot evolutionary fitness scoring and clinical pathogenicity prediction powered by <b>Meta ESM-2 Transformer Foundation Model</b>.</div>', unsafe_allow_html=True) | |
| PRESETS = { | |
| "TP53 p.Arg175His (Li-Fraumeni / Cancer Driver - Pathogenic)": { | |
| "gene": "TP53", "anno": "p.Arg175His", | |
| "wt": "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSD", | |
| "mut": "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTHVRAMAIYKQSQHMTEVVRRCPHHERCSDSD", | |
| "clinvar": "Pathogenic", "condition": "Li-Fraumeni Syndrome / Solid Tumors" | |
| }, | |
| "TP53 p.Pro47Pro (Synonymous Polymorphism - Benign)": { | |
| "gene": "TP53", "anno": "p.Pro47Pro", | |
| "wt": "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSD", | |
| "mut": "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSD", | |
| "clinvar": "Benign", "condition": "Non-pathogenic synonymous variant" | |
| }, | |
| "EGFR p.Leu858Arg (Non-Small Cell Lung Cancer Driver - Pathogenic)": { | |
| "gene": "EGFR", "anno": "p.Leu858Arg", | |
| "wt": "LKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSY", | |
| "mut": "LKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGRAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSY", | |
| "clinvar": "Pathogenic", "condition": "NSCLC / Erlotinib & Gefitinib Sensitizing" | |
| }, | |
| "BRAF p.Val600Glu (Melanoma / Colorectal Driver - Pathogenic)": { | |
| "gene": "BRAF", "anno": "p.Val600Glu", | |
| "wt": "LIDIWEIPDGQGQMILGKDVSSAKAVSEKGLRLIQGQTLSLKIDSTGLLLRSLSVTVFDLHRWGRDVQGDFVFYTAVVKVTKLGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQ", | |
| "mut": "LIDIWEIPDGQGQMILGKDVSSAKAVSEKGLRLIQGQTLSLKIDSTGLLLRSLSVTVFDLHRWGRDVQGDFVFYTAVVKVTKLGDFGLATEKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQ", | |
| "clinvar": "Pathogenic", "condition": "Cutaneous Melanoma / Vemurafenib target" | |
| }, | |
| "KRAS p.Gly12Asp (Pancreatic / Colorectal Driver - Pathogenic)": { | |
| "gene": "KRAS", "anno": "p.Gly12Asp", | |
| "wt": "MTEYKLVVVGAGGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQRVEDAFYTLVREIRQYRLKKISKEEKTPGCVKIKKCIIM", | |
| "mut": "MTEYKLVVVGADGVGKSALTIQLIQNHFVDEYDPTIEDSYRKQVVIDGETCLLDILDTAGQEEYSAMRDQYMRTGEGFLCVFAINNTKSFEDIHHYREQIKRVKDSEDVPMVLVGNKCDLPSRTVDTKQAQDLARSYGIPFIETSAKTRQRVEDAFYTLVREIRQYRLKKISKEEKTPGCVKIKKCIIM", | |
| "clinvar": "Pathogenic", "condition": "Pancreatic Ductal Adenocarcinoma" | |
| }, | |
| "BRCA1 p.Cys61Gly (Hereditary Breast & Ovarian Cancer - Pathogenic)": { | |
| "gene": "BRCA1", "anno": "p.Cys61Gly", | |
| "wt": "MDLSALRVEEVQNVINAMQKILECPICLELIKEPVSTKCDHIFCKFCMLKLLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLEYANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQETSLSVQLSNLGTVRTLRTKQRIQPQKTSVYIELGSDS", | |
| "mut": "MDLSALRVEEVQNVINAMQKILECPIGLELIKEPVSTKCDHIFCKFCMLKLLNQKKGPSQCPLCKNDITKRSLQESTRFSQLVEELLKIICAFQLDTGLEYANSYNFAKKENNSPEHLKDEVSIIQSMGYRNRAKRLLQSEPENPSLQETSLSVQLSNLGTVRTLRTKQRIQPQKTSVYIELGSDS", | |
| "clinvar": "Pathogenic", "condition": "Hereditary Breast & Ovarian Cancer (HBOC)" | |
| } | |
| } | |
| col_left, col_right = st.columns([1, 1], gap="large") | |
| with col_left: | |
| st.subheader("📋 Variant Specifications") | |
| preset_choice = st.selectbox( | |
| "Load Validated Clinical Benchmark:", | |
| list(PRESETS.keys()) + ["Custom Sequence Input"] | |
| ) | |
| if preset_choice in PRESETS: | |
| p = PRESETS[preset_choice] | |
| gene_name = st.text_input("Gene Symbol", p["gene"]) | |
| anno_name = st.text_input("Mutation HGVS", p["anno"]) | |
| wt_input = st.text_area("Wildtype (Reference) Sequence", p["wt"], height=110) | |
| mut_input = st.text_area("Mutant Sequence", p["mut"], height=110) | |
| st.caption(f"**ClinVar Ground Truth**: {p['clinvar']} | **Clinical Phenotype**: {p['condition']}") | |
| else: | |
| gene_name = st.text_input("Gene Symbol", "TP53") | |
| anno_name = st.text_input("Mutation HGVS", "p.Arg175His") | |
| wt_input = st.text_area("Wildtype (Reference) Sequence", "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPL...", height=110) | |
| mut_input = st.text_area("Mutant Sequence", "MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPL...", height=110) | |
| predict_btn = st.button("🚀 Run Foundation Model In Silico Scoring", type="primary", use_container_width=True) | |
| with col_right: | |
| st.subheader("📊 In Silico Prediction & Biophysical Disruption") | |
| if predict_btn or preset_choice in PRESETS: | |
| with st.spinner("Evaluating evolutionary constraint using Meta ESM-2..."): | |
| res = scorer.score_sequence_pair(wt_input, mut_input) | |
| is_path = "Pathogenic" in res["prediction"] | |
| color = "#ef4444" if is_path else "#10b981" | |
| badge = '<span class="badge-path">PATHOGENIC / DELETERIOUS</span>' if is_path else '<span class="badge-benign">BENIGN / TOLERATED</span>' | |
| st.markdown(f"### Classification: {badge}", unsafe_allow_html=True) | |
| m1, m2, m3 = st.columns(3) | |
| m1.metric("Pathogenicity Risk", f"{res['pathogenicity_prob']:.2%}") | |
| m2.metric("Zero-Shot LLR (ΔScore)", f"{res['zero_shot_llr_score']:+.4f}") | |
| m3.metric("Residue Site Entropy", f"{res['site_entropy']:.3f}") | |
| # Gauge Indicator | |
| fig = go.Figure(go.Indicator( | |
| mode="gauge+number", | |
| value=res["pathogenicity_prob"] * 100, | |
| domain={"x": [0, 1], "y": [0, 1]}, | |
| title={"text": "Pathogenicity Probability Index (%)", "font": {"size": 18}}, | |
| gauge={ | |
| "axis": {"range": [0, 100]}, | |
| "bar": {"color": color}, | |
| "steps": [ | |
| {"range": [0, 40], "color": "#dcfce7"}, | |
| {"range": [40, 70], "color": "#fef9c3"}, | |
| {"range": [70, 100], "color": "#fee2e2"} | |
| ], | |
| "threshold": { | |
| "line": {"color": "#1e293b", "width": 4}, | |
| "thickness": 0.8, | |
| "value": 50 | |
| } | |
| } | |
| )) | |
| fig.update_layout(height=240, margin=dict(l=20, r=20, t=35, b=10)) | |
| st.plotly_chart(fig, use_container_width=True) | |
| # Sequence Mutation Mapping | |
| st.markdown("#### 🧬 Residue Mutation Coordinate") | |
| st.write(f"• **Position**: Residue **{res['gene_position']}**") | |
| st.write(f"• **Wildtype Residue**: {res['wildtype_residue']} (Log-Likelihood: {res['log_likelihood_wt']})") | |
| st.write(f"• **Mutant Residue**: {res['mutant_residue']} (Log-Likelihood: {res['log_likelihood_mut']})") | |
| st.write(f"• **Delta LLR**: {res['zero_shot_llr_score']} *(Values > 1.5 indicate high evolutionary penalty)*") | |
| st.write(f"• **Foundation Model**: {res['foundation_model']}") | |
| st.divider() | |
| st.markdown("Developed by **Hardik Sood** (IIT BHU) | Open-Source on [GitHub](https://github.com/hardiksood21/variantllm)") | |