Add application file

Dockerfile

@@ -0,0 +1,13 @@
+FROM python:3.9
+
+RUN useradd -m -u 1000 user
+USER user
+ENV PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+COPY --chown=user ./requirements.txt requirements.txt
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+COPY --chown=user . /app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,83 @@
+import torch
+from tokenizers import Tokenizer
+from torch.utils.data import DataLoader
+import uvicorn
+from fastapi import FastAPI
+from pydantic import BaseModel, Field
+
+from model import CustomDataset, TransformerEncoder, load_model_to_cpu
+
+app = FastAPI()
+
+
+
+tag2id = {"O": 0, "olumsuz": 1, "nötr": 2, "olumlu": 3, "org": 4}
+id2tag = {value: key for key, value in tag2id.items()}
+
+device = torch.device('cpu')
+def predict_fonk(model, device, example, tokenizer):
+    model.to(device)
+    model.eval()
+    predictions = []
+
+    encodings_prdict = tokenizer.encode(example)
+
+    predict_texts = [encodings_prdict.tokens]
+    predict_input_ids = [encodings_prdict.ids]
+    predict_attention_masks = [encodings_prdict.attention_mask]
+    predict_token_type_ids = [encodings_prdict.type_ids]
+    prediction_labels = [encodings_prdict.type_ids]
+
+    predict_data = CustomDataset(predict_texts, predict_input_ids, predict_attention_masks, predict_token_type_ids,
+                                 prediction_labels)
+
+    predict_loader = DataLoader(predict_data, batch_size=1, shuffle=False)
+
+    with torch.no_grad():
+        for dataset in predict_loader:
+            batch_input_ids = dataset['input_ids'].to(device)
+            batch_att_mask = dataset['attention_mask'].to(device)
+
+            outputs = model(batch_input_ids, batch_att_mask)
+            logits = outputs.view(-1, outputs.size(-1))  # Flatten the outputs
+            _, predicted = torch.max(logits, 1)
+
+            # Ignore padding tokens for predictions
+            predictions.append(predicted)
+
+    results_list = []
+    entity_list = []
+    results_dict = {}
+    for i, (token, label, attention) in enumerate(zip(predict_loader.dataset[0]["text"], predictions[0].tolist(),
+                                       predict_attention_masks[0])):
+        if attention != 0 and label != 0 and label !=4 and token not in [sep for sepx in entity_list for sep in sepx.split()]:
+            for next_ones in predictions[0].tolist()[i+1:]:
+                i+=1
+                if next_ones == 4:
+                    token = token +" "+ predict_loader.dataset[0]["text"][i]
+                else:break
+            if token not in entity_list:
+                entity_list.append(token)
+                results_list.append({"entity":token,"sentiment":id2tag.get(label)})
+
+
+    results_dict["entity_list"] = entity_list
+    results_dict["results"] = results_list
+
+
+    return results_dict
+
+class Item(BaseModel):
+    text: str = Field(..., example="""Fiber 100mb SuperOnline kullanıcısıyım yaklaşık 2 haftadır @Twitch @Kick_Turkey gibi canlı yayın platformlarında 360p yayın izlerken donmalar yaşıyoruz.  Başka hiç bir operatörler bu sorunu yaşamazken ben parasını verip alamadığım hizmeti neden ödeyeyim ? @Turkcell """)
+
+@app.post("/predict/", response_model=dict)
+async def predict(item: Item):
+    model = TransformerEncoder()
+    model, start_epoch = load_model_to_cpu(model, "model.pth")
+    tokenizer = Tokenizer.from_file("tokenizer.json")
+
+    predict_list = predict_fonk(model=model, device=device, example=item.text, tokenizer=tokenizer)
+
+    #Buraya model'in çıktısı gelecek
+    #Çıktı formatı aşağıdaki örnek gibi olacak
+    return predict_list

requirements.txt

@@ -0,0 +1,5 @@
+torch==2.3.0
+tokenizers==0.13.3
+uvicorn
+fastapi
+pydantic