Update main.py

main.py

@@ -1,95 +1,123 @@
-import torch
-import torch.nn as nn
-import re
-from fastapi import FastAPI
-from pydantic import BaseModel
-
-# =====================================================
-# App
-# =====================================================
-app = FastAPI(title="Khmer Spell Correction API")
-
-# =====================================================
-# Utils
-# =====================================================
-def preprocess_khmer_text(text: str) -> str:
-    text = re.sub(r"\s+", " ", text)
-    text = re.sub(r"[^\u1780-\u17FF\u200B\u0020-\u007E]", "", text)
-    return text.strip()
-
-# =====================================================
-# Model
-# =====================================================
-class KhmerSpellLSTM(nn.Module):
-    def __init__(self, vocab_size, embedding_dim, hidden_dim):
-        super().__init__()
-        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
-        self.lstm = nn.LSTM(
-            embedding_dim,
-            hidden_dim,
-            batch_first=True,
-            bidirectional=True
-        )
-        self.fc = nn.Linear(hidden_dim * 2, vocab_size)
-
-    def forward(self, x):
-        x = self.embedding(x)
-        x, _ = self.lstm(x)
-        return self.fc(x)
-
-# =====================================================
-# Load Model ONCE
-# =====================================================
-device = torch.device("cpu")
-
-checkpoint = torch.load("khmer_spell_lstm.pth", map_location=device)
-
-char_to_idx = checkpoint["char_to_idx"]
-idx_to_char = {i: c for c, i in char_to_idx.items()}
-max_length = checkpoint["max_length"]
-
-model = KhmerSpellLSTM(
-    vocab_size=len(char_to_idx),
-    embedding_dim=128,
-    hidden_dim=256
-)
-
-model.load_state_dict(checkpoint["model_state_dict"])
-model.eval()
-
-# =====================================================
-# Inference
-# =====================================================
-def predict(text: str) -> str:
-    text = preprocess_khmer_text(text)
-    input_len = len(text)
-
-    seq = [char_to_idx.get(c, char_to_idx["<UNK>"]) for c in text]
-    seq += [char_to_idx["<PAD>"]] * (max_length - len(seq))
-    seq = torch.tensor(seq[:max_length]).unsqueeze(0)
-
-    with torch.no_grad():
-        out = model(seq)
-        pred = torch.argmax(out, dim=-1)[0][:input_len]
-
-    return "".join(idx_to_char[i.item()] for i in pred)
-
-# =====================================================
-# Schema
-# =====================================================
-class TextInput(BaseModel):
-    text: str
-
-# =====================================================
-# Routes
-# =====================================================
-@app.get("/")
-def health():
-    return {"status": "Khmer Spell API running"}
-
-@app.post("/predict")
-def spell_correct(data: TextInput):
-    return {
-        "input": data.text,
-        "output": predict(data.text)
-    }
+import torch
+import torch.nn as nn
+import re
+from fastapi import FastAPI
+from pydantic import BaseModel
+from fastapi.middleware.cors import CORSMiddleware
+
+# =====================================================
+# 1. FastAPI App
+# =====================================================
+app = FastAPI(
+    title="Khmer Spell Correction API",
+    version="1.0"
+)
+
+# Allow CORS for testing
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# =====================================================
+# 2. Utils
+# =====================================================
+def preprocess_khmer_text(text: str) -> str:
+    """Clean and normalize Khmer text."""
+    text = re.sub(r'\s+', ' ', text)
+    text = re.sub(r'[^\u1780-\u17FF\u200B\u0020-\u007E]', '', text)
+    return text.strip()
+
+# =====================================================
+# 3. Model Definition
+# =====================================================
+class KhmerSpellLSTM(nn.Module):
+    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers=2, dropout=0.3):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=0)
+        self.lstm = nn.LSTM(
+            embedding_dim,
+            hidden_dim,
+            num_layers=num_layers,
+            batch_first=True,
+            dropout=dropout if num_layers > 1 else 0,
+            bidirectional=True
+        )
+        # Match checkpoint fc
+        self.fc = nn.Sequential(
+            nn.Linear(hidden_dim * 2, hidden_dim),
+            nn.ReLU(),
+            nn.Dropout(dropout),
+            nn.Linear(hidden_dim, vocab_size)
+        )
+
+    def forward(self, x):
+        emb = self.embedding(x)
+        out, _ = self.lstm(emb)
+        return self.fc(out)
+
+# =====================================================
+# 4. Load Model ONCE
+# =====================================================
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+checkpoint = torch.load("result/khmer_spell_lstm.pth", map_location=device)
+
+char_to_idx = checkpoint["char_to_idx"]
+vocab = checkpoint.get("vocab", char_to_idx.keys())
+max_length = checkpoint["max_length"]
+idx_to_char = {i: c for c, i in char_to_idx.items()}
+
+model = KhmerSpellLSTM(
+    vocab_size=len(vocab),
+    embedding_dim=128,
+    hidden_dim=256
+).to(device)
+
+# Load weights
+model.load_state_dict(checkpoint["model_state_dict"])
+model.eval()
+print("✅ Khmer Spell LSTM loaded successfully")
+
+# =====================================================
+# 5. Inference Function
+# =====================================================
+def predict(text: str) -> str:
+    text = preprocess_khmer_text(text)
+    input_len = len(text)
+
+    seq = [char_to_idx.get(c, char_to_idx["<UNK>"]) for c in text]
+    seq += [char_to_idx["<PAD>"]] * (max_length - len(seq))
+    seq = torch.tensor(seq[:max_length]).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        out = model(seq)
+        pred = torch.argmax(out, dim=-1)[0]
+
+    # Keep the prediction same length as input
+    pred = pred[:input_len]
+
+    return "".join(idx_to_char[i.item()] for i in pred)
+
+# =====================================================
+# 6. API Schema
+# =====================================================
+class TextInput(BaseModel):
+    text: str
+
+# =====================================================
+# 7. Routes
+# =====================================================
+@app.get("/")
+def health_check():
+    return {"status": "Khmer Spell API running"}
+
+@app.post("/predict")
+def spell_correct(data: TextInput):
+    corrected_text = predict(data.text)
+    return {
+        "input": data.text,
+        "output": corrected_text
+    }