| """ |
| ╔══════════════════════════════════════════════════════════════╗ |
| ║ PharmaBridge — Cross-Medical-System Drug Intelligence ║ |
| ║ Hugging Face Spaces | Gradio 4.x | Master's Thesis ║ |
| ╚══════════════════════════════════════════════════════════════╝ |
| 7 Tabs: |
| 1. Smart Drug Search — TF-IDF cosine retrieval with cards UI |
| 2. Cross-System Compare — Side-by-side 5-system radar comparison |
| 3. Dataset Analytics — 3 sub-tabs of Plotly dashboards |
| 4. Drug Fingerprint — Single drug deep-dive profile |
| 5. FDA Live Intelligence — OpenFDA API (Labels / Events / NDC) |
| 6. AI Medical Q&A — HuggingFace Inference API (Mistral-7B) |
| 7. Drug Explorer — Paginated browse & filter table |
| """ |
|
|
| import gradio as gr |
| import pandas as pd |
| import numpy as np |
| import plotly.graph_objects as go |
| import plotly.express as px |
| from plotly.subplots import make_subplots |
| import joblib, re, os, requests, json, warnings |
| warnings.filterwarnings("ignore") |
|
|
| from sklearn.feature_extraction.text import TfidfVectorizer |
| from sklearn.metrics.pairwise import cosine_similarity |
|
|
| |
| |
| |
|
|
| def _clean(text): |
| if pd.isna(text): return "" |
| t = str(text).strip() |
| if t in ["FALSE","False","false","nan","NaN",""]: return "" |
| return re.sub(r"\s+"," ", re.sub(r"[^a-z0-9\s\+\-\./]"," ", t.lower())).strip() |
|
|
| def _build_text(row): |
| s = row["medical_system"] |
| d = _clean(row.get("Dosages Description","")) |
| g = _clean(row.get("Generic Name and Strength","")) |
| b = _clean(row.get("Brand Name","")) |
| n = _clean(row.get("Generic Name","")) |
| if s == "Allopathic": return " ".join(filter(None,[n,d,s.lower()])) |
| if s in ("Ayurvedic","Herbal"): return " ".join(filter(None,[g,d,s.lower()])) |
| if s == "Homeopathic": return " ".join(filter(None,[b,d,s.lower()])) |
| return " ".join(filter(None,[g,d,s.lower()])) |
|
|
| print("⏳ Loading PharmaBridge models…") |
| try: |
| VEC = joblib.load("models/tfidf_vectorizer.pkl") |
| MAT = joblib.load("models/tfidf_matrix.pkl") |
| DF = pd.read_csv("models/drug_database.csv") |
| print("✅ PKL models loaded.") |
| except Exception as e: |
| print(f"⚠️ PKL not found ({e}), rebuilding from CSV…") |
| raw = pd.read_csv("merged_pharma_dataset.csv") |
| DF = raw.copy() |
| DF["drug_text"] = DF.apply(_build_text, axis=1) |
| DF = DF.rename(columns={ |
| "Brand Name":"brand_name","Generic Name":"generic_name", |
| "Dosages Description":"dosage_form","Strength":"strength", |
| "Name of the Manufacturer":"manufacturer", |
| "Generic Name and Strength":"gns", |
| }) |
| VEC = TfidfVectorizer(ngram_range=(1,2),max_features=15000, |
| stop_words=None,sublinear_tf=True,min_df=1) |
| MAT = VEC.fit_transform(DF["drug_text"]) |
| print("✅ Rebuilt from CSV.") |
|
|
| |
| _REMAP = {"Brand Name":"brand_name","Generic Name":"generic_name", |
| "Dosages Description":"dosage_form","Strength":"strength", |
| "Name of the Manufacturer":"manufacturer","Generic Name and Strength":"gns"} |
| for o,n in _REMAP.items(): |
| if o in DF.columns and n not in DF.columns: |
| DF.rename(columns={o:n},inplace=True) |
| for c in ["brand_name","generic_name","dosage_form","strength","manufacturer","gns","drug_text"]: |
| if c not in DF.columns: DF[c] = "" |
| if "drug_text" not in DF.columns or DF["drug_text"].str.len().sum()==0: |
| DF["drug_text"] = DF.apply(_build_text, axis=1) |
|
|
| DF = DF.reset_index(drop=True) |
|
|
| SYSTEMS = ["All Systems","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"] |
| SC = {"Allopathic":"#3B82F6","Ayurvedic":"#10B981", |
| "Unani":"#F59E0B","Homeopathic":"#8B5CF6","Herbal":"#EF4444"} |
| EMOJI = {"Allopathic":"💊","Ayurvedic":"🌿","Unani":"☘️","Homeopathic":"💧","Herbal":"🌱"} |
|
|
| |
| _SYS_VC = DF["medical_system"].value_counts() |
| _DOS_VC = DF["dosage_form"].value_counts() |
| _MFR_VC = DF["manufacturer"].value_counts() |
| _SYS_MFR = DF.groupby("medical_system")["manufacturer"].nunique() |
| _FEAT = np.array(VEC.get_feature_names_out()) |
|
|
| |
| |
| |
|
|
| def _encode(q): |
| q2 = re.sub(r"[^a-z0-9\s\+\-\./]"," ",q.lower()) |
| return VEC.transform([re.sub(r"\s+"," ",q2).strip()]) |
|
|
| def _recommend(query, system, top_n, min_s): |
| sims = cosine_similarity(_encode(query), MAT).flatten() |
| if system not in ("All Systems","All",""): |
| mask = DF["medical_system"]==system |
| sims[~mask.values]=0 |
| idx=[i for i in sims.argsort()[-(top_n*4):][::-1] if sims[i]>=min_s][:top_n] |
| if not idx: return pd.DataFrame() |
| r=DF.iloc[idx].copy(); r["score"]=[round(float(sims[i]),4) for i in idx] |
| return r.sort_values("score",ascending=False).reset_index(drop=True) |
|
|
| def _cross(query, tps): |
| sims = cosine_similarity(_encode(query), MAT).flatten() |
| rows=[] |
| for sys in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]: |
| sc=sims.copy(); sc[~(DF["medical_system"]==sys).values]=0 |
| for i in [i for i in sc.argsort()[-tps:][::-1] if sims[i]>0.01]: |
| d=DF.iloc[i].to_dict(); d["score"]=round(float(sims[i]),4); rows.append(d) |
| if not rows: return pd.DataFrame() |
| return (pd.DataFrame(rows) |
| .sort_values(["medical_system","score"],ascending=[True,False]) |
| .reset_index(drop=True)) |
|
|
| |
| |
| |
|
|
| def tab1(query, system, top_n, min_s): |
| if not query.strip(): |
| return '<div class="ph">🔍 Type a drug name, compound, or symptom above and press Search</div>', None, "" |
|
|
| r = _recommend(query, system, int(top_n), float(min_s)) |
| if r.empty: |
| return f'<div class="ph">No results found for <b>{query}</b>. Try lowering the similarity threshold.</div>', None, "" |
|
|
| cards = f'<div class="rh">Found <b>{len(r)}</b> results for "<b>{query}</b>"</div><div class="grid">' |
| for _, row in r.iterrows(): |
| sys = str(row.get("medical_system","")) |
| c = SC.get(sys,"#6B7280") |
| em = EMOJI.get(sys,"💊") |
| bn = str(row.get("brand_name","—")) |
| gn = str(row.get("gns","")) or str(row.get("generic_name","—")) |
| dos = str(row.get("dosage_form","—")) |
| mfr = str(row.get("manufacturer","—"))[:38] |
| sc_v = float(row.get("score",0)) |
| pct = int(sc_v*100) |
| cards += f""" |
| <div class="card" style="border-left:4px solid {c}"> |
| <div class="ch"> |
| <span class="sbadge" style="background:{c}18;color:{c};border:1px solid {c}35">{em} {sys}</span> |
| <span class="spct" style="background:{c}12;color:{c}">{pct}%</span> |
| </div> |
| <div class="bn">{bn}</div> |
| <div class="gn">{gn[:70]+'…' if len(gn)>70 else gn}</div> |
| <div class="meta">💊 {dos} · 🏭 {mfr}</div> |
| <div class="bar"><div class="fill" style="width:{pct}%;background:{c}"></div></div> |
| </div>""" |
| cards += "</div>" |
|
|
| fig = px.bar( |
| r.head(15), x="score", y="brand_name", color="medical_system", |
| color_discrete_map=SC, orientation="h", |
| labels={"score":"Similarity Score","brand_name":""}, |
| title=f'Similarity Scores — "{query}"', |
| ) |
| fig.update_layout( |
| height=max(340,len(r.head(15))*30+90), |
| paper_bgcolor="rgba(0,0,0,0)", plot_bgcolor="rgba(0,0,0,0)", |
| font=dict(family="Inter,sans-serif",size=11), |
| legend=dict(orientation="h",yanchor="bottom",y=1.02,title=None), |
| margin=dict(l=0,r=10,t=50,b=10), yaxis=dict(autorange="reversed"), |
| xaxis=dict(range=[0,1],gridcolor="#f1f5f9"), |
| ) |
| dist = r["medical_system"].value_counts().to_dict() |
| stat = " · ".join(f"**{k}** {v}" for k,v in dist.items()) |
| return cards, fig, f"📊 {stat}" |
|
|
|
|
| |
| |
| |
|
|
| def tab2(query, tps): |
| if not query.strip(): |
| return '<div class="ph">Enter a query to compare drugs across all 5 medical traditions</div>', None |
|
|
| r = _cross(query, int(tps)) |
| if r.empty: |
| return '<div class="ph">No cross-system results found.</div>', None |
|
|
| html = f'<div class="cph">Cross-system view for <b>"{query}"</b></div><div class="cgrid">' |
| for sys in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]: |
| sub = r[r["medical_system"]==sys] |
| c = SC[sys]; em=EMOJI[sys] |
| html += f'<div class="scol" style="border-top:3px solid {c}"><div class="stitle" style="color:{c}">{em} {sys}</div>' |
| if sub.empty: |
| html += '<div class="nr">No match found</div>' |
| else: |
| for _,row in sub.iterrows(): |
| bn = str(row.get("brand_name","—")) |
| gn = str(row.get("gns","")) or str(row.get("generic_name","—")) |
| dos = str(row.get("dosage_form","—")) |
| sc_ = int(float(row.get("score",0))*100) |
| html += f"""<div class="cc" style="border-left:3px solid {c}38"> |
| <div class="cbn">{bn}</div> |
| <div class="cgn">{gn[:48]+'…' if len(gn)>48 else gn}</div> |
| <div class="cm">{dos} · {sc_}%</div> |
| <div class="sbar"><div class="sfill" style="width:{sc_}%;background:{c}"></div></div> |
| </div>""" |
| html += "</div>" |
| html += "</div>" |
|
|
| |
| avgs={s: float(r[r["medical_system"]==s]["score"].mean()) if not r[r["medical_system"]==s].empty else 0 |
| for s in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]} |
| cats=list(avgs.keys()); vals=list(avgs.values()) |
| fig=go.Figure(go.Scatterpolar( |
| r=vals+[vals[0]], theta=cats+[cats[0]], fill="toself", |
| fillcolor="rgba(59,130,246,0.12)", line=dict(color="#3B82F6",width=2.5), |
| marker=dict(size=9,color=[SC[s] for s in cats]+[SC[cats[0]]]), |
| )) |
| fig.update_layout( |
| polar=dict(radialaxis=dict(visible=True,range=[0,1],gridcolor="#e5e7eb"), |
| angularaxis=dict(gridcolor="#e5e7eb",tickfont=dict(size=12))), |
| title=dict(text=f'Cross-System Radar — "{query}"',font=dict(size=13,color="#1e293b")), |
| paper_bgcolor="rgba(0,0,0,0)", font=dict(family="Inter,sans-serif"), |
| height=380, showlegend=False, margin=dict(l=50,r=50,t=60,b=30), |
| ) |
| return html, fig |
|
|
|
|
| |
| |
| |
|
|
| def _overview_fig(): |
| fig=make_subplots(rows=2,cols=3, |
| subplot_titles=["System Share","Top 12 Dosage Forms","Manufacturers per System", |
| "Top 15 Manufacturers","System × Dosage Heatmap","TF-IDF Vocab Share"], |
| specs=[[{"type":"domain"},{"type":"xy"},{"type":"xy"}], |
| [{"type":"xy"},{"type":"xy"},{"type":"domain"}]], |
| vertical_spacing=0.14,horizontal_spacing=0.08) |
|
|
| |
| fig.add_trace(go.Pie( |
| labels=_SYS_VC.index.tolist(),values=_SYS_VC.values.tolist(),hole=0.55, |
| marker=dict(colors=[SC.get(s,"#aaa") for s in _SYS_VC.index], |
| line=dict(color="white",width=2.5)), |
| textinfo="label+percent",textfont=dict(size=10),showlegend=False, |
| ),row=1,col=1) |
|
|
| |
| td=_DOS_VC.head(12) |
| fig.add_trace(go.Bar( |
| x=td.values[::-1],y=td.index[::-1].tolist(),orientation="h", |
| marker=dict(color=px.colors.sequential.Blues_r[:12],line=dict(color="white",width=1)), |
| text=[f"{v:,}" for v in td.values[::-1]],textposition="outside",showlegend=False, |
| ),row=1,col=2) |
|
|
| |
| fig.add_trace(go.Bar( |
| x=_SYS_MFR.index.tolist(),y=_SYS_MFR.values.tolist(), |
| marker=dict(color=[SC.get(s,"#aaa") for s in _SYS_MFR.index], |
| line=dict(color="white",width=2)), |
| text=_SYS_MFR.values.tolist(),textposition="outside",showlegend=False, |
| ),row=1,col=3) |
|
|
| |
| tm=_MFR_VC.head(15) |
| fig.add_trace(go.Bar( |
| y=[m[:28] for m in tm.index[::-1].tolist()],x=tm.values[::-1].tolist(), |
| orientation="h", |
| marker=dict(color=tm.values[::-1].tolist(),colorscale="Viridis", |
| showscale=False,line=dict(color="white",width=1)), |
| showlegend=False, |
| ),row=2,col=1) |
|
|
| |
| top8=_DOS_VC.head(8).index.tolist() |
| sysl=["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"] |
| piv=pd.crosstab(DF["medical_system"],DF["dosage_form"]) |
| z=[[int(piv[d].get(s,0)) if d in piv.columns else 0 for d in top8] for s in sysl] |
| fig.add_trace(go.Heatmap( |
| z=z,x=[d[:12] for d in top8],y=sysl,colorscale="YlOrRd", |
| text=z,texttemplate="%{text}",textfont=dict(size=9), |
| showscale=True,colorbar=dict(thickness=10,x=0.65,len=0.42), |
| ),row=2,col=2) |
|
|
| |
| vtoks={s:int((np.asarray(MAT[(DF["medical_system"]==s).values].mean(axis=0)).flatten()>0.001).sum()) |
| for s in ["Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"]} |
| fig.add_trace(go.Pie( |
| labels=list(vtoks.keys()),values=list(vtoks.values()),hole=0.5, |
| marker=dict(colors=[SC.get(s,"#aaa") for s in vtoks], |
| line=dict(color="white",width=2)), |
| textinfo="label+value",textfont=dict(size=10),showlegend=False, |
| ),row=2,col=3) |
|
|
| fig.update_layout( |
| height=720,paper_bgcolor="rgba(0,0,0,0)",plot_bgcolor="rgba(0,0,0,0)", |
| font=dict(family="Inter,sans-serif",size=11), |
| title=dict(text="PharmaBridge — Dataset Intelligence Dashboard", |
| font=dict(size=16,color="#1e293b"),x=0.5), |
| margin=dict(l=10,r=10,t=80,b=10), |
| ) |
| fig.update_xaxes(showgrid=True,gridcolor="#f1f5f9",zeroline=False) |
| fig.update_yaxes(showgrid=False) |
| return fig |
|
|
| def _deep_fig(sel): |
| sub = DF if sel=="All" else DF[DF["medical_system"]==sel] |
| c = SC.get(sel,"#3B82F6") |
| fig=make_subplots(rows=2,cols=2, |
| subplot_titles=[f"Top 20 Compounds ({sel})","Dosage Form Split", |
| "Top 10 Manufacturers","Brand Count Comparison"], |
| specs=[[{"type":"xy"},{"type":"domain"}],[{"type":"xy"},{"type":"xy"}]], |
| vertical_spacing=0.16,horizontal_spacing=0.10) |
|
|
| |
| if sel=="Homeopathic": comp=sub["brand_name"].value_counts().head(20) |
| elif sel=="Allopathic": comp=sub["generic_name"].dropna().value_counts().head(20) |
| else: comp=sub["gns"].dropna().value_counts().head(20) |
| fig.add_trace(go.Bar( |
| x=comp.values[::-1].tolist(),y=comp.index[::-1].tolist(),orientation="h", |
| marker=dict(color=c,opacity=0.85,line=dict(color="white",width=1)), |
| text=comp.values[::-1].tolist(),textposition="outside",showlegend=False, |
| ),row=1,col=1) |
|
|
| |
| dos=sub["dosage_form"].value_counts().head(8) |
| fig.add_trace(go.Pie( |
| labels=dos.index.tolist(),values=dos.values.tolist(),hole=0.48, |
| marker=dict(colors=px.colors.qualitative.Set3[:len(dos)], |
| line=dict(color="white",width=2)), |
| textinfo="label+percent",textfont=dict(size=10),showlegend=False, |
| ),row=1,col=2) |
|
|
| |
| mf=sub["manufacturer"].value_counts().head(10) |
| fig.add_trace(go.Bar( |
| x=mf.values[::-1].tolist(),y=[m[:26] for m in mf.index[::-1].tolist()], |
| orientation="h", |
| marker=dict(color=mf.values[::-1].tolist(),colorscale="Blues", |
| showscale=False,line=dict(color="white",width=1)), |
| showlegend=False, |
| ),row=2,col=1) |
|
|
| |
| bc=DF.groupby("medical_system")["brand_name"].nunique().sort_values(ascending=False) |
| fig.add_trace(go.Bar( |
| x=bc.index.tolist(),y=bc.values.tolist(), |
| marker=dict(color=[c if s==sel else "#cbd5e1" for s in bc.index], |
| line=dict(color="white",width=2)), |
| text=bc.values.tolist(),textposition="outside",showlegend=False, |
| ),row=2,col=2) |
|
|
| fig.update_layout( |
| height=680,paper_bgcolor="rgba(0,0,0,0)",plot_bgcolor="rgba(0,0,0,0)", |
| font=dict(family="Inter,sans-serif",size=11), |
| title=dict(text=f"Deep Dive: {sel}",font=dict(size=15,color="#1e293b"),x=0.5), |
| margin=dict(l=10,r=10,t=70,b=10), |
| ) |
| fig.update_xaxes(showgrid=True,gridcolor="#f1f5f9",zeroline=False) |
| fig.update_yaxes(showgrid=False) |
| return fig |
|
|
| def _treemap_fig(): |
| samp=DF.groupby(["medical_system","dosage_form"]).size().reset_index(name="count") |
| samp=samp[samp["count"]>=5] |
| fig=px.treemap(samp,path=["medical_system","dosage_form"],values="count", |
| color="medical_system",color_discrete_map=SC, |
| title="Drug Hierarchy: Medical System → Dosage Form") |
| fig.update_traces(textinfo="label+value+percent parent",textfont=dict(size=12)) |
| fig.update_layout(height=520,paper_bgcolor="rgba(0,0,0,0)", |
| font=dict(family="Inter,sans-serif",size=12), |
| title=dict(font=dict(size=15,color="#1e293b"),x=0.5), |
| margin=dict(l=10,r=10,t=60,b=10)) |
| return fig |
|
|
| def tab3_deep_update(sel): |
| return _deep_fig(sel) |
|
|
|
|
| |
| |
| |
|
|
| def tab4_fingerprint(brand_query): |
| """Search for a specific drug and show a rich visual profile card + radar of its TF-IDF feature weights.""" |
| if not brand_query.strip(): |
| return '<div class="ph">Enter a brand name to see its full drug profile</div>', None |
|
|
| |
| sims = cosine_similarity(_encode(brand_query), MAT).flatten() |
| idx = int(sims.argsort()[-1]) |
| row = DF.iloc[idx] |
| sc_v = float(sims[idx]) |
|
|
| if sc_v < 0.01: |
| return f'<div class="ph">No drug found matching "<b>{brand_query}</b>".</div>', None |
|
|
| sys_n = str(row.get("medical_system","")) |
| c = SC.get(sys_n,"#6B7280") |
| em = EMOJI.get(sys_n,"💊") |
| bn = str(row.get("brand_name","—")) |
| gn = str(row.get("gns","")) or str(row.get("generic_name","—")) |
| dos = str(row.get("dosage_form","—")) |
| mfr = str(row.get("manufacturer","—")) |
| clu = str(row.get("cluster","—")) |
| dart = str(row.get("DAR","—")) if "DAR" in row.index else "—" |
| txt = str(row.get("drug_text","")) |
|
|
| |
| sib_mask = (DF["medical_system"]==sys_n) & (DF["gns"]==str(row.get("gns",""))) |
| sib_count = sib_mask.sum()-1 |
|
|
| html = f""" |
| <div class="fp-card" style="border:2px solid {c}40;background:white;border-radius:16px;overflow:hidden"> |
| <div class="fp-banner" style="background:linear-gradient(135deg,{c},{c}99);padding:20px 24px;color:white"> |
| <div style="font-size:0.85rem;opacity:0.85;margin-bottom:4px">{em} {sys_n}</div> |
| <div style="font-size:1.7rem;font-weight:800;letter-spacing:-0.5px">{bn}</div> |
| <div style="font-size:0.95rem;opacity:0.9;margin-top:4px">{gn[:80]}</div> |
| <div style="margin-top:12px;background:rgba(255,255,255,0.2);border-radius:20px;padding:5px 14px; |
| display:inline-block;font-size:0.8rem;font-weight:600"> |
| {int(sc_v*100)}% match confidence |
| </div> |
| </div> |
| <div style="padding:20px 24px;display:grid;grid-template-columns:1fr 1fr;gap:14px"> |
| <div class="fp-row"><span class="fp-k">💊 Dosage Form</span><span class="fp-v">{dos}</span></div> |
| <div class="fp-row"><span class="fp-k">🏭 Manufacturer</span><span class="fp-v">{mfr[:40]}</span></div> |
| <div class="fp-row"><span class="fp-k">🧬 Medical System</span><span class="fp-v">{sys_n}</span></div> |
| <div class="fp-row"><span class="fp-k">📂 Cluster</span><span class="fp-v">#{clu}</span></div> |
| <div class="fp-row"><span class="fp-k">📋 DAR Number</span><span class="fp-v">{dart}</span></div> |
| <div class="fp-row"><span class="fp-k">👥 Same-compound drugs</span><span class="fp-v">{sib_count}</span></div> |
| </div> |
| <div style="padding:0 24px 20px;font-size:0.82rem;color:#64748b"> |
| <b>Drug Text (TF-IDF input):</b> <code style="background:#f1f5f9;padding:3px 8px;border-radius:6px">{txt[:120]}</code> |
| </div> |
| </div>""" |
|
|
| |
| vec_row = MAT[idx] |
| feat_idx = np.asarray(vec_row.todense()).flatten().argsort()[-20:][::-1] |
| feat_scores = np.asarray(vec_row.todense()).flatten()[feat_idx] |
| feat_labels = _FEAT[feat_idx] |
| mask = feat_scores > 0 |
| feat_labels = feat_labels[mask]; feat_scores = feat_scores[mask] |
|
|
| fig = go.Figure(go.Bar( |
| x=feat_scores[::-1], y=feat_labels[::-1], |
| orientation="h", |
| marker=dict( |
| color=feat_scores[::-1], |
| colorscale=[[0,"#dbeafe"],[1,c]], |
| showscale=False, |
| line=dict(color="white",width=1), |
| ), |
| text=[f"{v:.3f}" for v in feat_scores[::-1]], |
| textposition="outside", |
| )) |
| fig.update_layout( |
| title=dict(text=f"TF-IDF Feature Fingerprint: {bn}", |
| font=dict(size=13,color="#1e293b")), |
| height=max(300, len(feat_labels)*28+80), |
| paper_bgcolor="rgba(0,0,0,0)",plot_bgcolor="rgba(0,0,0,0)", |
| font=dict(family="Inter,sans-serif",size=11), |
| margin=dict(l=10,r=60,t=50,b=10), |
| xaxis=dict(gridcolor="#f1f5f9",title="TF-IDF Weight"), |
| yaxis=dict(title=""), |
| ) |
| return html, fig |
|
|
|
|
| |
| |
| |
|
|
| FDA_NAME_MAP={ |
| "Paracetamol":"acetaminophen","Azithromycin":"azithromycin", |
| "Ciprofloxacin":"ciprofloxacin","Amoxicillin":"amoxicillin", |
| "Omeprazole":"omeprazole","Metformin":"metformin", |
| "Atorvastatin":"atorvastatin","Amlodipine":"amlodipine", |
| "Ceftriaxone":"ceftriaxone","Diclofenac":"diclofenac sodium", |
| "Esomeprazole":"esomeprazole","Cefixime":"cefixime", |
| "Salbutamol":"albuterol","Ibuprofen":"ibuprofen", |
| "Metronidazole":"metronidazole","Cefuroxime":"cefuroxime", |
| } |
|
|
| def _fda_fetch(drug, endpoint): |
| term=FDA_NAME_MAP.get(drug,drug.lower()) |
| base=f"https://api.fda.gov/drug/{endpoint}.json" |
| for field in [f"openfda.generic_name:{term}",f"openfda.brand_name:{term}"]: |
| try: |
| r=requests.get(base,params={"search":field,"limit":"3"},timeout=9) |
| if r.status_code==200: |
| res=r.json().get("results",[]) |
| if res: return res, term |
| except: pass |
| return [], term |
|
|
| def tab5_fda(drug, ep_label): |
| if not drug.strip(): |
| return '<div class="ph">🏥 Enter a drug name to fetch live FDA data</div>' |
| ep_map={"Drug Labels":"label","Adverse Events (FAERS)":"event","NDC Directory":"ndc"} |
| ep=ep_map.get(ep_label,"label") |
| results,term=_fda_fetch(drug,ep) |
|
|
| if not results: |
| return f"""<div class="fda-miss"> |
| <div style="font-size:2.5rem;margin-bottom:12px">🔍</div> |
| <div><b>No FDA data found for "{drug}"</b></div> |
| <div style="color:#64748b;font-size:0.88rem;margin-top:8px;line-height:1.7"> |
| This drug may not be in the US FDA database (common for Bangladesh-registry drugs).<br> |
| <b>Try:</b> Paracetamol · Azithromycin · Ciprofloxacin · Omeprazole · Metformin · Ibuprofen |
| </div></div>""" |
|
|
| html=f"""<div class="fda-hdr"> |
| <span class="fda-badge">🇺🇸 FDA {ep_label}</span> |
| <b>{drug}</b> → searched as <code>{term}</code> |
| <span class="fda-cnt">{len(results)} record(s)</span> |
| </div>""" |
|
|
| if ep=="label": |
| for i,res in enumerate(results[:3],1): |
| o=res.get("openfda",{}) |
| brand=", ".join(o.get("brand_name",["—"])[:2]) |
| gen =", ".join(o.get("generic_name",["—"])[:2]) |
| mfr =", ".join(o.get("manufacturer_name",["—"])[:1]) |
| purp =str(res.get("purpose",["—"])[0])[:280] if res.get("purpose") else "—" |
| ind =str(res.get("indications_and_usage",["—"])[0])[:380] if res.get("indications_and_usage") else "—" |
| warn =str(res.get("warnings",["—"])[0])[:280] if res.get("warnings") else "—" |
| html+=f"""<div class="fda-card"> |
| <div class="fda-num">📄 Record {i}</div> |
| <table class="fda-tbl"> |
| <tr><td class="fk">Brand Name</td><td>{brand}</td></tr> |
| <tr><td class="fk">Generic Name</td><td>{gen}</td></tr> |
| <tr><td class="fk">Manufacturer</td><td>{mfr}</td></tr> |
| <tr><td class="fk">Purpose</td><td>{purp}</td></tr> |
| <tr><td class="fk">Indications</td><td>{ind}</td></tr> |
| <tr><td class="fk">Warnings</td><td>{warn}</td></tr> |
| </table></div>""" |
|
|
| elif ep=="event": |
| for i,res in enumerate(results[:3],1): |
| pt=res.get("patient",{}) |
| rxn=", ".join(r.get("reactionmeddrapt","") for r in pt.get("reaction",[])[:6]) |
| drg=", ".join(d.get("medicinalproduct","") for d in pt.get("drug",[])[:4]) |
| sev="⚠️ Serious" if res.get("serious")=="1" else "ℹ️ Non-Serious" |
| html+=f"""<div class="fda-card"> |
| <div class="fda-num">Event {i} — {sev}</div> |
| <table class="fda-tbl"> |
| <tr><td class="fk">Reactions</td><td>{rxn or '—'}</td></tr> |
| <tr><td class="fk">Drugs Involved</td><td>{drg or '—'}</td></tr> |
| </table></div>""" |
|
|
| elif ep=="ndc": |
| for i,res in enumerate(results[:3],1): |
| html+=f"""<div class="fda-card"> |
| <div class="fda-num">NDC {i}</div> |
| <table class="fda-tbl"> |
| <tr><td class="fk">NDC Code</td><td>{res.get('product_ndc','—')}</td></tr> |
| <tr><td class="fk">Brand</td><td>{res.get('brand_name','—')}</td></tr> |
| <tr><td class="fk">Generic</td><td>{res.get('generic_name','—')}</td></tr> |
| <tr><td class="fk">Dosage Form</td><td>{res.get('dosage_form','—')}</td></tr> |
| <tr><td class="fk">Route</td><td>{res.get('route','—')}</td></tr> |
| <tr><td class="fk">Labeler</td><td>{res.get('labeler_name','—')}</td></tr> |
| </table></div>""" |
| return html |
|
|
|
|
| |
| |
| |
|
|
| SYS_PROMPT=( |
| "You are PharmaBridge AI — a knowledgeable, friendly pharmaceutical assistant. " |
| "You help healthcare professionals and students understand drug information, " |
| "pharmacology, traditional medicine (Ayurvedic, Unani, Homeopathic, Herbal), " |
| "drug interactions, and the Bangladesh drug registry. " |
| "Be concise, accurate, and always note that answers are educational, " |
| "not a substitute for professional medical advice." |
| ) |
|
|
| HF_MODELS=[ |
| "mistralai/Mistral-7B-Instruct-v0.3", |
| "HuggingFaceH4/zephyr-7b-beta", |
| "google/flan-t5-xxl", |
| ] |
|
|
| def tab6_ai(question, history): |
| if not question.strip(): |
| return history, "" |
| history=history or [] |
|
|
| prompt=f"<s>[INST] {SYS_PROMPT}\n\nQuestion: {question} [/INST]" |
| headers={"Content-Type":"application/json"} |
| answer="" |
|
|
| for model_url in [f"https://api-inference.huggingface.co/models/{m}" for m in HF_MODELS]: |
| payload={ |
| "inputs": prompt, |
| "parameters":{"max_new_tokens":500,"temperature":0.65, |
| "top_p":0.9,"repetition_penalty":1.1, |
| "return_full_text":False}, |
| } |
| |
| if "flan" in model_url: |
| payload={"inputs":f"As a pharmacist, answer clearly: {question}", |
| "parameters":{"max_new_tokens":350}} |
| try: |
| r=requests.post(model_url,headers=headers,json=payload,timeout=28) |
| if r.status_code==200: |
| d=r.json() |
| txt=(d[0].get("generated_text","") if isinstance(d,list) else d.get("generated_text","")).strip() |
| if len(txt)>30: |
| answer=txt; break |
| except: continue |
|
|
| if not answer: |
| answer=( |
| "⚠️ The AI model is warming up (HuggingFace free tier cold-start). " |
| "Please wait ~20 seconds and try again.\n\n" |
| "**Meanwhile**, you can:\n" |
| "- Use the **Smart Search** tab to look up this drug directly\n" |
| "- Use the **FDA Live Data** tab for official drug information" |
| ) |
|
|
| history.append((question, answer)) |
| return history, "" |
|
|
| def tab6_clear(): |
| return [], "" |
|
|
|
|
| |
| |
| |
|
|
| _ALL_DOS=["All"]+sorted(DF["dosage_form"].dropna().unique().tolist()) |
|
|
| def _dos_choices(sys): |
| if sys=="All": |
| return gr.update(choices=_ALL_DOS, value="All") |
| opts=["All"]+sorted(DF[DF["medical_system"]==sys]["dosage_form"].dropna().unique().tolist()) |
| return gr.update(choices=opts, value="All") |
|
|
| def tab7_explore(system, dosage, search, page): |
| sub=DF.copy() |
| if system!="All": sub=sub[sub["medical_system"]==system] |
| if dosage !="All": sub=sub[sub["dosage_form"]==dosage] |
| if search.strip(): |
| t=search.lower().strip() |
| sub=sub[sub["brand_name"].str.lower().str.contains(t,na=False)| |
| sub["gns"].str.lower().str.contains(t,na=False)| |
| sub["generic_name"].str.lower().str.contains(t,na=False)| |
| sub["manufacturer"].str.lower().str.contains(t,na=False)] |
|
|
| total=len(sub); PG=20 |
| page=max(1,int(page)); maxp=max(1,(total+PG-1)//PG); page=min(page,maxp) |
| sl=sub.iloc[(page-1)*PG:page*PG] |
|
|
| if sl.empty: |
| return '<div class="ph">No records match your filters.</div>', "0 records" |
|
|
| rows="" |
| for _,row in sl.iterrows(): |
| sys_n=str(row.get("medical_system","")) |
| c=SC.get(sys_n,"#6B7280"); em=EMOJI.get(sys_n,"💊") |
| bn=str(row.get("brand_name","—")) |
| gn=str(row.get("gns","")) or str(row.get("generic_name","—")) |
| dos_v=str(row.get("dosage_form","—")) |
| mfr=str(row.get("manufacturer","—"))[:36] |
| rows+=f"""<tr> |
| <td><b>{bn}</b></td> |
| <td style="max-width:190px">{gn[:46]}</td> |
| <td>{dos_v}</td> |
| <td><span class="sb2" style="background:{c}18;color:{c};border:1px solid {c}30">{em} {sys_n}</span></td> |
| <td style="color:#64748b">{mfr}</td> |
| </tr>""" |
|
|
| tbl=f"""<table class="xtbl"> |
| <thead><tr><th>Brand Name</th><th>Compound / Identity</th> |
| <th>Dosage Form</th><th>System</th><th>Manufacturer</th></tr></thead> |
| <tbody>{rows}</tbody></table>""" |
|
|
| return tbl, f"Page **{page}** / {maxp} · **{total:,}** records" |
|
|
|
|
| |
| |
| |
|
|
| CSS=""" |
| @import url('https://fonts.googleapis.com/css2?family=Inter:ital,wght@0,300;0,400;0,500;0,600;0,700;0,800;1,400&display=swap'); |
| *{box-sizing:border-box} |
| body,.gradio-container{font-family:'Inter',sans-serif!important;background:#f0f4f8!important} |
| |
| /* ── HEADER ─────────────────────────────────────────────── */ |
| .app-hdr{ |
| background:linear-gradient(135deg,#0f172a 0%,#1e3a8a 45%,#0369a1 100%); |
| border-radius:18px;padding:28px 32px;margin-bottom:4px;color:#fff; |
| box-shadow:0 10px 40px rgba(30,58,138,.35); |
| } |
| .app-title{font-size:2.1rem;font-weight:800;letter-spacing:-1px;margin:0} |
| .app-sub{font-size:1rem;opacity:.82;margin:6px 0 0} |
| .hbadges{display:flex;gap:8px;margin-top:14px;flex-wrap:wrap} |
| .hbadge{background:rgba(255,255,255,.16);border:1px solid rgba(255,255,255,.28); |
| border-radius:20px;padding:4px 13px;font-size:.78rem;font-weight:500} |
| .stats-row{display:flex;gap:10px;margin-top:16px;flex-wrap:wrap} |
| .stat{background:rgba(255,255,255,.12);border-radius:12px;padding:8px 16px;text-align:center;min-width:88px} |
| .sn{font-size:1.45rem;font-weight:800;display:block} |
| .sl{font-size:.7rem;opacity:.78;text-transform:uppercase;letter-spacing:.5px} |
| |
| /* ── TABS ────────────────────────────────────────────────── */ |
| .tab-nav button{font-weight:500!important;font-size:.88rem!important;border-radius:8px 8px 0 0!important} |
| .tab-nav button.selected{color:#1d4ed8!important;border-bottom:3px solid #1d4ed8!important;font-weight:700!important} |
| |
| /* ── INPUTS ──────────────────────────────────────────────── */ |
| .gr-input,textarea,.gr-dropdown select{ |
| border-radius:10px!important;border:1.5px solid #e2e8f0!important; |
| font-family:'Inter',sans-serif!important;transition:border-color .2s!important; |
| } |
| .gr-input:focus,textarea:focus{border-color:#3b82f6!important;box-shadow:0 0 0 3px rgba(59,130,246,.1)!important} |
| .gr-button-primary{ |
| background:linear-gradient(135deg,#1d4ed8,#0891b2)!important; |
| border:none!important;border-radius:10px!important;font-weight:700!important; |
| letter-spacing:.2px!important;box-shadow:0 4px 14px rgba(29,78,216,.3)!important; |
| transition:transform .15s,box-shadow .15s!important; |
| } |
| .gr-button-primary:hover{transform:translateY(-1px)!important;box-shadow:0 6px 22px rgba(29,78,216,.4)!important} |
| |
| /* ── PLACEHOLDERS ────────────────────────────────────────── */ |
| .ph{text-align:center;color:#94a3b8;padding:60px 20px;font-size:.98rem; |
| background:#f8fafc;border-radius:14px;border:2px dashed #e2e8f0} |
| |
| /* ── RESULT CARDS ────────────────────────────────────────── */ |
| .rh{font-size:.93rem;color:#475569;padding:10px 0 14px; |
| border-bottom:1px solid #e2e8f0;margin-bottom:14px} |
| .grid{display:grid;grid-template-columns:repeat(auto-fill,minmax(270px,1fr));gap:12px} |
| .card{background:#fff;border-radius:13px;padding:14px 16px; |
| box-shadow:0 1px 4px rgba(0,0,0,.06);transition:transform .15s,box-shadow .15s} |
| .card:hover{transform:translateY(-2px);box-shadow:0 5px 18px rgba(0,0,0,.10)} |
| .ch{display:flex;justify-content:space-between;align-items:center;margin-bottom:8px} |
| .sbadge{font-size:.71rem;font-weight:600;padding:3px 9px;border-radius:20px;white-space:nowrap} |
| .spct{font-size:.74rem;font-weight:700;padding:3px 9px;border-radius:20px} |
| .bn{font-size:1.05rem;font-weight:700;color:#1e293b;margin-bottom:4px} |
| .gn{font-size:.81rem;color:#64748b;margin-bottom:9px;min-height:1.2em} |
| .meta{font-size:.77rem;color:#94a3b8;margin-bottom:10px;line-height:1.8} |
| .bar{height:4px;background:#f1f5f9;border-radius:2px;overflow:hidden} |
| .fill{height:100%;border-radius:2px;transition:width .4s} |
| |
| /* ── CROSS COMPARE ───────────────────────────────────────── */ |
| .cph{font-size:.96rem;color:#475569;padding:10px 0 16px;font-weight:500} |
| .cgrid{display:grid;grid-template-columns:repeat(5,1fr);gap:11px} |
| @media(max-width:900px){.cgrid{grid-template-columns:repeat(2,1fr)}} |
| .scol{background:#fff;border-radius:13px;padding:14px;box-shadow:0 1px 4px rgba(0,0,0,.06)} |
| .stitle{font-weight:700;font-size:.93rem;margin-bottom:12px} |
| .nr{color:#94a3b8;font-size:.84rem;padding:10px 0} |
| .cc{padding:10px;margin-bottom:8px;border-radius:9px;background:#f8fafc} |
| .cbn{font-weight:700;font-size:.88rem;color:#1e293b} |
| .cgn{font-size:.77rem;color:#64748b;margin:3px 0} |
| .cm{font-size:.74rem;color:#94a3b8} |
| .sbar{height:3px;background:#f1f5f9;border-radius:2px;overflow:hidden;margin-top:6px} |
| .sfill{height:100%;border-radius:2px} |
| |
| /* ── FINGERPRINT ─────────────────────────────────────────── */ |
| .fp-banner{border-radius:0} |
| .fp-row{display:flex;flex-direction:column;background:#f8fafc;border-radius:10px;padding:10px 14px} |
| .fp-k{font-size:.74rem;color:#64748b;font-weight:600;text-transform:uppercase;letter-spacing:.4px} |
| .fp-v{font-size:.95rem;color:#1e293b;font-weight:500;margin-top:2px} |
| |
| /* ── FDA ─────────────────────────────────────────────────── */ |
| .fda-hdr{background:linear-gradient(135deg,#eff6ff,#e0f2fe);border-radius:11px; |
| padding:14px 18px;margin-bottom:14px;display:flex;align-items:center; |
| gap:10px;flex-wrap:wrap;font-size:.88rem;color:#1e293b} |
| .fda-badge{background:#1d4ed8;color:#fff;padding:4px 11px;border-radius:20px; |
| font-size:.77rem;font-weight:600} |
| .fda-cnt{margin-left:auto;background:#dcfce7;color:#166534;padding:3px 10px; |
| border-radius:20px;font-size:.77rem;font-weight:600} |
| .fda-miss{text-align:center;padding:40px;color:#64748b;background:#f8fafc; |
| border-radius:14px;border:2px dashed #e2e8f0} |
| .fda-card{background:#fff;border-radius:13px;padding:18px;margin-bottom:12px; |
| box-shadow:0 1px 4px rgba(0,0,0,.06)} |
| .fda-num{font-weight:700;font-size:.88rem;color:#1d4ed8;margin-bottom:10px} |
| .fda-tbl{width:100%;border-collapse:collapse;font-size:.84rem} |
| .fda-tbl tr{border-bottom:1px solid #f1f5f9} |
| .fda-tbl tr:last-child{border-bottom:none} |
| .fk{color:#64748b;font-weight:600;padding:6px 14px 6px 0;white-space:nowrap; |
| vertical-align:top;width:130px} |
| .fda-tbl td:last-child{color:#1e293b;padding:6px 0;line-height:1.55} |
| |
| /* ── CHATBOT ─────────────────────────────────────────────── */ |
| .chatbot{border-radius:13px!important;border:1.5px solid #e2e8f0!important} |
| |
| /* ── EXPLORER TABLE ──────────────────────────────────────── */ |
| .xtbl{width:100%;border-collapse:collapse;font-size:.83rem} |
| .xtbl thead{background:linear-gradient(135deg,#0f172a,#1d4ed8);color:#fff} |
| .xtbl th{padding:11px 14px;text-align:left;font-weight:600;letter-spacing:.3px} |
| .xtbl tbody tr{border-bottom:1px solid #f1f5f9;transition:background .15s} |
| .xtbl tbody tr:hover{background:#f8fafc} |
| .xtbl td{padding:9px 14px;color:#1e293b;vertical-align:top} |
| .sb2{font-size:.71rem;font-weight:600;padding:2px 8px;border-radius:20px;white-space:nowrap} |
| |
| code{background:#f1f5f9;padding:2px 7px;border-radius:5px;font-size:.84em;color:#0891b2} |
| """ |
|
|
| |
| |
| |
|
|
| HEADER = f""" |
| <div class="app-hdr"> |
| <div class="app-title">💊 PharmaBridge</div> |
| <div class="app-sub">Cross-Medical-System Drug Intelligence Engine · Bangladesh National Drug Registry</div> |
| <div class="hbadges"> |
| <span class="hbadge">🔬 TF-IDF + Cosine Similarity</span> |
| <span class="hbadge">🧠 SVD + K-Means Clustering</span> |
| <span class="hbadge">🌐 OpenFDA Live API</span> |
| <span class="hbadge">🤖 Mistral-7B AI Assistant</span> |
| <span class="hbadge">📊 Interactive Dashboards</span> |
| </div> |
| <div class="stats-row"> |
| <div class="stat"><span class="sn">53,584</span><span class="sl">Total Drugs</span></div> |
| <div class="stat"><span class="sn">5</span><span class="sl">Med. Systems</span></div> |
| <div class="stat"><span class="sn">725</span><span class="sl">Manufacturers</span></div> |
| <div class="stat"><span class="sn">12,311</span><span class="sl">TF-IDF Features</span></div> |
| <div class="stat"><span class="sn">95.5%</span><span class="sl">Precision@10</span></div> |
| <div class="stat"><span class="sn">0.2159</span><span class="sl">Silhouette</span></div> |
| </div> |
| </div> |
| """ |
|
|
| with gr.Blocks(css=CSS, title="PharmaBridge", theme=gr.themes.Base( |
| primary_hue=gr.themes.colors.blue, |
| font=gr.themes.GoogleFont("Inter"), |
| )) as app: |
|
|
| gr.HTML(HEADER) |
|
|
| with gr.Tabs(elem_classes="tab-nav"): |
|
|
| |
| with gr.Tab("🔍 Smart Search"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=4): |
| t1q = gr.Textbox(label="Search Query", |
| placeholder="Try: Azithromycin, Ashwagandha, nux vomica, sharbat amrood, paracetamol fever…", |
| lines=1) |
| with gr.Column(scale=1): |
| t1sys = gr.Dropdown(choices=SYSTEMS, value="All Systems", label="System") |
| with gr.Column(scale=1): |
| t1btn = gr.Button("🔍 Search", variant="primary", scale=1) |
| with gr.Row(): |
| t1n = gr.Slider(5,50,value=12,step=1,label="Max Results") |
| t1s = gr.Slider(0.0,0.5,value=0.04,step=0.01,label="Min Similarity") |
| t1stat = gr.Markdown("") |
| t1cards = gr.HTML('<div class="ph">🔍 Enter a drug name, compound, or symptom above</div>') |
| t1chart = gr.Plot(label="Score Distribution") |
|
|
| t1btn.click(tab1,[t1q,t1sys,t1n,t1s],[t1cards,t1chart,t1stat]) |
| t1q.submit(tab1,[t1q,t1sys,t1n,t1s],[t1cards,t1chart,t1stat]) |
|
|
| gr.Examples([ |
| ["Azithromycin 500mg","Allopathic"], |
| ["Ashwagandha capsule","Ayurvedic"], |
| ["Nux Vomica liquid","Homeopathic"], |
| ["Sharbat Amrood","Unani"], |
| ["Moringa leaf powder","Herbal"], |
| ["antibiotic tablet","All Systems"], |
| ["digestive capsule","All Systems"], |
| ], inputs=[t1q,t1sys], label="Quick Examples") |
|
|
| |
| with gr.Tab("⚖️ Cross-System Compare"): |
| with gr.Row(equal_height=True): |
| with gr.Column(scale=5): |
| t2q = gr.Textbox(label="Query", |
| placeholder="e.g. pain relief tablet, digestive liver, sleep anxiety, blood pressure…", |
| lines=1) |
| with gr.Column(scale=1): |
| t2n = gr.Slider(1,5,value=3,step=1,label="Results / System") |
| with gr.Column(scale=1): |
| t2btn = gr.Button("⚖️ Compare", variant="primary") |
| t2cards = gr.HTML('<div class="ph">Compare the same therapeutic need across all 5 medical traditions simultaneously</div>') |
| t2radar = gr.Plot(label="Cross-System Similarity Radar") |
|
|
| t2btn.click(tab2,[t2q,t2n],[t2cards,t2radar]) |
| t2q.submit(tab2,[t2q,t2n],[t2cards,t2radar]) |
| gr.Examples([ |
| ["digestive liver tablet"],["pain anti-inflammatory"], |
| ["antibiotic infection"],["blood pressure"], |
| ["cough respiratory"],["sleep anxiety stress"], |
| ], inputs=[t2q]) |
|
|
| |
| with gr.Tab("📊 Dataset Analytics"): |
| with gr.Tabs(): |
| with gr.Tab("🌐 Overview Dashboard"): |
| ov_btn = gr.Button("📊 Render Dashboard", variant="primary") |
| ov_fig = gr.Plot() |
| ov_btn.click(_overview_fig,[],[ov_fig]) |
| app.load(_overview_fig,[],[ov_fig]) |
|
|
| with gr.Tab("🔎 System Deep Dive"): |
| with gr.Row(): |
| dd_sys = gr.Dropdown( |
| choices=["All","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"], |
| value="Allopathic", label="Select System") |
| dd_btn = gr.Button("Analyze", variant="primary") |
| dd_fig = gr.Plot() |
| dd_btn.click(_deep_fig,[dd_sys],[dd_fig]) |
| dd_sys.change(_deep_fig,[dd_sys],[dd_fig]) |
| app.load(lambda:_deep_fig("Allopathic"),[],[dd_fig]) |
|
|
| with gr.Tab("🗺️ Treemap Explorer"): |
| tm_btn = gr.Button("🗺️ Render Treemap", variant="primary") |
| tm_fig = gr.Plot() |
| tm_btn.click(_treemap_fig,[],[tm_fig]) |
| app.load(_treemap_fig,[],[tm_fig]) |
|
|
| |
| with gr.Tab("🧬 Drug Fingerprint"): |
| gr.Markdown(""" |
| ### Single Drug Deep-Dive |
| Search for any drug to see its **full profile card** plus a bar chart of its |
| top TF-IDF feature weights — the exact tokens driving its similarity scores. |
| """) |
| with gr.Row(equal_height=True): |
| fp_q = gr.Textbox(label="Brand Name or Compound", |
| placeholder="e.g. Azithromycin, Ashwagandha, Nux Vomica, Sharbat Amrood…", lines=1) |
| fp_btn = gr.Button("🧬 Profile", variant="primary") |
| fp_card = gr.HTML('<div class="ph">🧬 Enter a drug or compound name to generate its fingerprint</div>') |
| fp_fig = gr.Plot(label="TF-IDF Feature Fingerprint") |
|
|
| fp_btn.click(tab4_fingerprint,[fp_q],[fp_card,fp_fig]) |
| fp_q.submit(tab4_fingerprint,[fp_q],[fp_card,fp_fig]) |
| gr.Examples([ |
| ["Azithromycin"],["Ashwagandha"],["Nux Vomica"], |
| ["Sharbat Amrood"],["Moringa"],["Paracetamol"], |
| ], inputs=[fp_q]) |
|
|
| |
| with gr.Tab("🏥 FDA Live Data"): |
| gr.Markdown("> **Live OpenFDA API** — US drug labels, adverse events (FAERS), and NDC records. " |
| "~40% of Bangladesh registry drugs appear here. Bangladeshi names auto-mapped to FDA terms.") |
| with gr.Row(equal_height=True): |
| fda_drug = gr.Textbox(label="Drug Name", |
| placeholder="Paracetamol, Azithromycin, Ciprofloxacin, Omeprazole, Metformin…", lines=1) |
| fda_ep = gr.Radio(["Drug Labels","Adverse Events (FAERS)","NDC Directory"], |
| value="Drug Labels", label="FDA Database") |
| fda_btn = gr.Button("🔎 Fetch", variant="primary") |
| fda_out = gr.HTML('<div class="ph">🏥 Enter a drug name and click Fetch</div>') |
| fda_btn.click(tab5_fda,[fda_drug,fda_ep],[fda_out]) |
| fda_drug.submit(tab5_fda,[fda_drug,fda_ep],[fda_out]) |
| gr.Examples([["Paracetamol"],["Azithromycin"],["Ciprofloxacin"], |
| ["Omeprazole"],["Metformin"],["Ibuprofen"]], inputs=[fda_drug]) |
|
|
| |
| with gr.Tab("🤖 AI Medical Q&A"): |
| gr.Markdown(""" |
| ### PharmaBridge AI — Pharmaceutical Q&A |
| Powered by **Mistral-7B-Instruct** via HuggingFace Inference API (free, no key needed). |
| Ask anything about drugs, pharmacology, traditional medicine, or the Bangladesh registry. |
| |
| > ⚠️ Educational only — not a substitute for professional medical advice. Model may take ~20s to cold-start. |
| """) |
| ai_bot = gr.Chatbot(label="PharmaBridge AI", height=450, elem_classes="chatbot") |
| with gr.Row(): |
| ai_inp = gr.Textbox(label="Your Question", lines=2, scale=5, |
| placeholder="e.g. What is Ashwagandha used for? / Side effects of Azithromycin? / What is Unani medicine?") |
| with gr.Column(scale=1): |
| ai_send = gr.Button("Send 💬", variant="primary") |
| ai_clear = gr.Button("Clear 🗑️") |
| ai_send.click(tab6_ai,[ai_inp,ai_bot],[ai_bot,ai_inp]) |
| ai_inp.submit(tab6_ai,[ai_inp,ai_bot],[ai_bot,ai_inp]) |
| ai_clear.click(tab6_clear,[],[ai_bot,ai_inp]) |
| gr.Examples([ |
| ["What is Ashwagandha used for in Ayurvedic medicine?"], |
| ["Explain Unani medicine and its traditional formulations"], |
| ["What are the common side effects of Azithromycin?"], |
| ["How does TF-IDF cosine similarity work for drug retrieval?"], |
| ["What is Homeopathic potency and how are remedies prepared?"], |
| ["Compare Allopathic and Herbal medicine approaches"], |
| ], inputs=[ai_inp]) |
|
|
| |
| with gr.Tab("📋 Drug Explorer"): |
| with gr.Row(): |
| ex_sys = gr.Dropdown(["All","Allopathic","Ayurvedic","Unani","Homeopathic","Herbal"], |
| value="All", label="System") |
| ex_dos = gr.Dropdown(choices=_ALL_DOS, value="All", label="Dosage Form") |
| ex_srch = gr.Textbox(label="Search", placeholder="Brand, compound, manufacturer…") |
| ex_pg = gr.Number(value=1, label="Page", minimum=1, precision=0) |
| ex_btn = gr.Button("🔍 Browse Database", variant="primary") |
| ex_info = gr.Markdown("") |
| ex_tbl = gr.HTML('<div class="ph">Click Browse to explore all 53,584 drug records</div>') |
|
|
| ex_sys.change(_dos_choices,[ex_sys],[ex_dos]) |
| ex_btn.click(tab7_explore,[ex_sys,ex_dos,ex_srch,ex_pg],[ex_tbl,ex_info]) |
| ex_srch.submit(tab7_explore,[ex_sys,ex_dos,ex_srch,ex_pg],[ex_tbl,ex_info]) |
|
|
| |
| with gr.Tab("ℹ️ About"): |
| gr.Markdown(f""" |
| ## PharmaBridge — Cross-Medical-System Drug Intelligence |
| |
| **PharmaBridge** is a master's thesis project — the first NLP-based drug recommendation system |
| spanning all 5 major South Asian pharmaceutical traditions simultaneously using the |
| Bangladesh National Drug Registry (53,584 records). |
| |
| --- |
| |
| ### Dataset Composition |
| | Medical System | Records | Share | |
| |---|---|---| |
| | Allopathic | 36,254 | 67.7% | |
| | Unani | 8,460 | 15.8% | |
| | Ayurvedic | 5,262 | 9.8% | |
| | Homeopathic | 2,580 | 4.8% | |
| | Herbal | 1,028 | 1.9% | |
| | **Total** | **53,584** | **100%** | |
| |
| ### Technical Architecture |
| | Component | Configuration | |
| |---|---| |
| | Vectorization | TF-IDF, bigrams (1,2), max_features=15,000, sublinear_tf=True | |
| | Retrieval | Cosine Similarity on sparse matrix (53,584 × 12,311) | |
| | Dim. Reduction | TruncatedSVD, 50 components, 26.2% variance | |
| | Clustering | K-Means K=10 (elbow-selected), Silhouette=0.2159 | |
| |
| ### Evaluation Results |
| | Metric | Value | |
| |---|---| |
| | Precision@5 | 97.00% | |
| | Precision@10 | 95.50% | |
| | Precision@20 | 90.55% | |
| | Silhouette Score | 0.2159 | |
| |
| ### App Features |
| | Tab | Feature | |
| |---|---| |
| | 🔍 Smart Search | TF-IDF cosine retrieval with rich card UI + bar chart | |
| | ⚖️ Cross-System Compare | Side-by-side 5-system view + radar chart | |
| | 📊 Dataset Analytics | Overview dashboard, deep-dive, treemap | |
| | 🧬 Drug Fingerprint | Single drug profile + TF-IDF feature bar chart | |
| | 🏥 FDA Live Data | OpenFDA labels / adverse events / NDC lookup | |
| | 🤖 AI Medical Q&A | Mistral-7B via HuggingFace Inference API | |
| | 📋 Drug Explorer | Paginated browse & filter across all 53,584 records | |
| |
| --- |
| > **Disclaimer:** For research and educational purposes only. |
| > Not intended for clinical decision-making. |
| > Always consult a qualified healthcare professional for medical advice. |
| """) |
|
|
| if __name__ == "__main__": |
| app.launch() |
|
|
