Create app.py

app.py

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+# FastAPI app for HF Spaces: 3 sentiment models + tiny test UI
+from fastapi import FastAPI
+from fastapi.responses import HTMLResponse
+from pydantic import BaseModel
+from typing import List, Dict, Any
+
+try:
+    import torch
+    DEVICE = 0 if torch.cuda.is_available() else -1
+except Exception:
+    DEVICE = -1
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
+
+# ---- Models ----
+FABSA_ID   = "Anudeep-Narala/fabsa-roberta-sentiment"            # 3-class
+TWITTER_ID = "cardiffnlp/twitter-roberta-base-sentiment-latest"  # 3-class
+MOOD_ID    = "Priyanshuchaudhary2425/MoodMeter-sentimental-analysis"  # 2-class (pos/neg)
+
+def load_pipe(model_id: str) -> TextClassificationPipeline:
+    tok = AutoTokenizer.from_pretrained(model_id)
+    mdl = AutoModelForSequenceClassification.from_pretrained(model_id)
+    return TextClassificationPipeline(
+        model=mdl, tokenizer=tok, device=DEVICE,
+        return_all_scores=True, truncation=True
+    )
+
+print("Loading models…")
+fabsa   = load_pipe(FABSA_ID)
+twitter = load_pipe(TWITTER_ID)
+mood    = load_pipe(MOOD_ID)
+print("Models ready.")
+
+def norm3(scores: List[Dict[str, Any]]):
+    """Map list[{label,score}] -> (pred, scores3, top, margin)."""
+    out = {"negative": 0.0, "neutral": 0.0, "positive": 0.0}
+    for e in scores:
+        lbl = e["label"].lower()
+        s = float(e["score"])
+        if "neg" in lbl or lbl == "label_0": out["negative"] = s
+        elif "neu" in lbl or lbl == "label_1": out["neutral"]  = s
+        elif "pos" in lbl or lbl == "label_2": out["positive"] = s
+    pred = max(out, key=out.get)
+    vals = sorted(out.values(), reverse=True)
+    top, margin = float(out[pred]), float(vals[0]-vals[1])
+    return pred, out, top, margin
+
+def norm2(scores: List[Dict[str, Any]]):
+    """Embed 2-class model into 3-class dict (neutral=0)."""
+    d = {"negative": 0.0, "positive": 0.0}
+    for e in scores:
+        lbl = e["label"].lower()
+        s = float(e["score"])
+        if "neg" in lbl or lbl == "label_0": d["negative"] = s
+        elif "pos" in lbl or lbl == "label_1": d["positive"] = s
+    pred = "negative" if d["negative"] >= d["positive"] else "positive"
+    top = float(max(d.values()))
+    margin = float(abs(d["negative"] - d["positive"]))
+    out3 = {"negative": d["negative"], "neutral": 0.0, "positive": d["positive"]}
+    return pred, out3, top, margin
+
+def fuse(fabsa_label: str, twitter_label: str) -> str:
+    # Ensemble rule you asked for:
+    # FABSA=neg -> neg; else if Twitter=neu -> neu; else Twitter
+    if fabsa_label == "negative": return "negative"
+    if twitter_label == "neutral": return "neutral"
+    return twitter_label
+
+app = FastAPI(title="HF Space — Sentiment Inference (FABSA + MoodMeter + Twitter)")
+
+class PredictIn(BaseModel):
+    text: str
+
+class BatchIn(BaseModel):
+    texts: List[str]
+
+@app.get("/health")
+def health():
+    return {"ok": True, "device": DEVICE}
+
+@app.post("/predict")
+def predict(inp: PredictIn):
+    t = (inp.text or "").strip()
+    f_raw = fabsa(t)[0]
+    t_raw = twitter(t)[0]
+    m_raw = mood(t)[0]
+
+    f_pred, f_scores, f_top, f_margin = norm3(f_raw)
+    t_pred, t_scores, t_top, t_margin = norm3(t_raw)
+    m_pred, m_scores, m_top, m_margin = norm2(m_raw)
+
+    return {
+        "text": t,
+        "fabsa":   {"label": f_pred, "scores": f_scores, "top": f_top, "margin": f_margin},
+        "twitter": {"label": t_pred, "scores": t_scores, "top": t_top, "margin": t_margin},
+        "mood":    {"label": m_pred, "scores": m_scores, "top": m_top, "margin": m_margin},
+        "ensemble": {"label": fuse(f_pred, t_pred)}
+    }
+
+@app.post("/batch")
+def batch(inp: BatchIn):
+    texts = [(x or "").strip() for x in inp.texts]
+    f_raw = fabsa(texts, batch_size=16)
+    t_raw = twitter(texts, batch_size=16)
+    m_raw = mood(texts, batch_size=16)
+
+    out = []
+    for i, t in enumerate(texts):
+        f_pred, f_scores, f_top, f_margin = norm3(f_raw[i])
+        t_pred, t_scores, t_top, t_margin = norm3(t_raw[i])
+        m_pred, m_scores, m_top, m_margin = norm2(m_raw[i])
+        out.append({
+            "text": t,
+            "fabsa":   {"label": f_pred, "scores": f_scores, "top": f_top, "margin": f_margin},
+            "twitter": {"label": t_pred, "scores": t_scores, "top": t_top, "margin": t_margin},
+            "mood":    {"label": m_pred, "scores": m_scores, "top": m_top, "margin": m_margin},
+            "ensemble": {"label": fuse(f_pred, t_pred)}
+        })
+    return {"items": out}
+
+# --- Super-simple test page (no backend needed) ---
+INDEX_HTML = """<!doctype html>
+<html lang="en"><head>
+<meta charset="utf-8"/>
+<meta name="viewport" content="width=device-width,initial-scale=1"/>
+<title>Sentiment Space — Quick Test</title>
+<style>
+  body{font-family: system-ui, Arial, sans-serif; background:#0b0b0b; color:#eee; padding:24px; max-width:900px; margin:0 auto;}
+  .card{border:1px solid #333; border-radius:12px; padding:16px; background:#111; margin-top:16px;}
+  textarea{width:100%; background:#0e0e0e; color:#eee; border:1px solid #333; border-radius:8px; padding:12px;}
+  button{background:#4F46E5; color:#fff; border:none; padding:10px 14px; border-radius:8px; cursor:pointer;}
+  pre{background:#0e0e0e; padding:12px; border-radius:8px; overflow:auto;}
+  .row{display:grid; grid-template-columns: 1fr 1fr; gap:16px;}
+</style>
+</head><body>
+  <h1>Sentiment Model Space — Quick Test</h1>
+  <div class="card">
+    <textarea id="txt" rows="5" placeholder="Type something like: I feel exhausted and nothing seems to help."></textarea>
+    <div style="margin-top:12px; display:flex; gap:12px;">
+      <button onclick="run()">Predict</button>
+      <button onclick="demo()">Demo Text</button>
+    </div>
+    <div id="status" style="opacity:.7; margin-top:8px;"></div>
+  </div>
+
+  <div class="row">
+    <div class="card"><h3>FABSA</h3><pre id="fabsa"></pre></div>
+    <div class="card"><h3>Twitter-RoBERTa</h3><pre id="twitter"></pre></div>
+  </div>
+  <div class="row">
+    <div class="card"><h3>MoodMeter (2-class)</h3><pre id="mood"></pre></div>
+    <div class="card"><h3>Ensemble</h3><pre id="ens"></pre></div>
+  </div>
+
+<script>
+async function run(){
+  const s = document.getElementById('txt').value.trim();
+  if(!s){ alert('Enter some text'); return; }
+  set('#status','Predicting...')
+  try{
+    const r = await fetch('/predict', {
+      method:'POST', headers:{'Content-Type':'application/json'},
+      body: JSON.stringify({text:s})
+    });
+    const j = await r.json();
+    set('#fabsa', fmt(j.fabsa));
+    set('#twitter', fmt(j.twitter));
+    set('#mood', fmt(j.mood));
+    set('#ens', JSON.stringify(j.ensemble, null, 2));
+    set('#status','Done.')
+  }catch(e){
+    set('#status','Error: '+e.message)
+  }
+}
+function set(sel, val){ document.querySelector(sel).textContent = typeof val==='string'? val : JSON.stringify(val,null,2); }
+function fmt(x){
+  if(!x) return '';
+  const s = x.scores||{};
+  return JSON.stringify({
+    label: x.label,
+    neg: round(s.negative), neu: round(s.neutral), pos: round(s.positive),
+    top: round(x.top), margin: round(x.margin)
+  }, null, 2);
+}
+function round(v){ return (v==null? null : Math.round(v*1000)/1000); }
+function demo(){
+  document.getElementById('txt').value = "It’s 3 a.m. again. I’m staring at the ceiling replaying everything I might fail tomorrow.";
+}
+</script>
+</body></html>
+"""
+
+@app.get("/", response_class=HTMLResponse)
+def index():
+    return HTMLResponse(INDEX_HTML)