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app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+import torch.nn as nn
+import numpy as np
+import re
+
+# ===============================
+# App Init
+# ===============================
+app = FastAPI(title="GoEmotions Sentiment API", version="1.0")
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+# ===============================
+# Emotion Mapping
+# ===============================
+emotion_map = [
+    "admiration","amusement","anger","annoyance","approval","caring","confusion",
+    "curiosity","desire","disappointment","disapproval","disgust","embarrassment",
+    "excitement","fear","gratitude","grief","joy","love","nervousness","optimism",
+    "pride","realization","relief","remorse","sadness","surprise","neutral"
+]
+
+POSITIVE_EMOTIONS = {
+    "admiration","amusement","approval","caring","desire","excitement",
+    "gratitude","joy","love","optimism","pride","relief"
+}
+
+NEGATIVE_EMOTIONS = {
+    "anger","annoyance","disappointment","disapproval","disgust","embarrassment",
+    "fear","grief","nervousness","remorse","sadness"
+}
+
+NEUTRAL_EMOTIONS = {
+    "confusion","curiosity","realization","surprise","neutral"
+}
+
+
+# ===============================
+# Text Utils
+# ===============================
+def simple_tokenize(text):
+    return text.split()
+
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r'[^a-z0-9\s]', ' ', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+
+# ===============================
+# Model Definition
+# ===============================
+class GoEmotionsLSTM(nn.Module):
+    def __init__(self, vocab_size, embed_dim=200, hidden_dim=256, num_classes=28, num_layers=2):
+        super().__init__()
+
+        self.embeddings = nn.Embedding(vocab_size, embed_dim, padding_idx=0)
+
+        self.lstm = nn.LSTM(
+            input_size=embed_dim,
+            hidden_size=hidden_dim,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=0.2,
+            bidirectional=True
+        )
+
+        self.fc = nn.Linear(hidden_dim * 2, num_classes)
+
+    def forward(self, x):
+        x = self.embeddings(x)
+        _, (h, _) = self.lstm(x)
+
+        h_forward = h[-2]
+        h_backward = h[-1]
+
+        h_cat = torch.cat((h_forward, h_backward), dim=1)
+        out = self.fc(h_cat)
+
+        return out
+
+
+# ===============================
+# Globals (Loaded Once)
+# ===============================
+model = None
+vocab = None
+max_len = None
+
+
+# ===============================
+# Load Model at Startup
+# ===============================
+@app.on_event("startup")
+def load_model():
+    global model, vocab, max_len
+
+    print("Loading GoEmotions BiLSTM model...")
+
+    checkpoint = torch.load("goemotions_bilstm_checkpoint.pth", map_location=DEVICE)
+
+    vocab = checkpoint["vocab"]
+    max_len = checkpoint["max_len"]
+
+    model = GoEmotionsLSTM(vocab_size=len(vocab))
+    model.load_state_dict(checkpoint["model_state"])
+    model.to(DEVICE)
+    model.eval()
+
+    print("Model loaded successfully.")
+
+
+# ===============================
+# Request Schema
+# ===============================
+class PredictRequest(BaseModel):
+    text: str
+
+
+# ===============================
+# Status Endpoint
+# ===============================
+@app.get("/status")
+def status():
+    if model is None:
+        return {"status": "loading"}
+    return {"status": "ok", "model_loaded": True}
+
+
+# ===============================
+# Sentiment Aggregation Logic
+# ===============================
+def aggregate_sentiment(probs):
+    pos_score = 0.0
+    neg_score = 0.0
+    neu_score = 0.0
+
+    for i, p in enumerate(probs):
+        emotion = emotion_map[i]
+        if emotion in POSITIVE_EMOTIONS:
+            pos_score += p
+        elif emotion in NEGATIVE_EMOTIONS:
+            neg_score += p
+        else:
+            neu_score += p
+
+    if pos_score > neg_score and pos_score > neu_score:
+        return "Positive", pos_score
+    elif neg_score > pos_score and neg_score > neu_score:
+        return "Negative", neg_score
+    else:
+        return "Neutral", neu_score
+
+
+# ===============================
+# Prediction Endpoint
+# ===============================
+@app.post("/predict")
+def predict(req: PredictRequest):
+    text = clean_text(req.text)
+    tokens = simple_tokenize(text)
+
+    # Convert tokens to indices
+    seq = [vocab.get(tok, 1) for tok in tokens]  # <UNK> = 1
+
+    # Pad / truncate
+    if len(seq) < max_len:
+        seq += [vocab["<PAD>"]] * (max_len - len(seq))
+    else:
+        seq = seq[:max_len]
+
+    x = torch.tensor([seq], dtype=torch.long).to(DEVICE)
+
+    with torch.no_grad():
+        logits = model(x)
+        probs = torch.sigmoid(logits).squeeze(0).cpu().numpy()
+
+    sentiment, score = aggregate_sentiment(probs)
+
+    return {
+        "sentiment": sentiment,
+        "confidence": round(float(score) * 100, 2)
+    }

goemotions_bilstm_checkpoint.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2061697f00e13a048b56bfd5b8ce721ba5cdd91143ce5a1d4e1e6a272ff7944d
+size 16386991