started backend model

api.py

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+from fastapi import FastAPI
+from classifier.Albert_latest import get_model
+
+app = FastAPI()
+model = get_model()
+
+@app.post("/predict")
+async def predict(issue: str):
+    predictions = model.predict(issue)
+    print(f"Predictions: {predictions}")
+    id = predictions
+    print(f"Predictions: {predictions}")
+    return {
+        "priority 1": str(predictions[0]),
+        "priority 2": str(predictions[1]),
+        "priority 3": str(predictions[2]),
+        "priority 4": str(predictions[3]),
+        "priority 5": str(predictions[4])
+    }

classifier/Albert_latest.py

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+from transformers import AlbertTokenizer, AlbertForSequenceClassification
+import torch
+
+class Model:
+    def __init__(self, model_weights):
+        self.tokenizer = AlbertTokenizer.from_pretrained('albert-base-v2')
+        self.model = AlbertForSequenceClassification.from_pretrained('albert-base-v2', num_labels=2)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        
+        # Load the checkpoint
+        checkpoint = torch.load(model_weights, map_location=self.device)
+        
+        # Load the model's state dictionary
+        self.model.load_state_dict(checkpoint['model_state_dict'])
+        self.currepoch = checkpoint['epoch']
+        self.loss = checkpoint['loss']
+        print(f"Loaded model state: Current epoch {self.currepoch}, current loss {self.loss}")
+        
+        self.model.to(self.device)
+        self.model.eval()
+
+    def predict(self, text):
+        inputs = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512)
+        inputs = {k: v.to(self.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+        
+        logits = outputs.logits
+        print(f"logits: {logits}")
+        predictions = torch.nn.functional.softmax(logits, dim=-1)
+        return predictions[0].tolist()
+
+model_instance = None
+model_weights = "../assets/albert_sentiment_checkpoint_58.pt"
+
+def get_model():
+    global model_instance
+    if model_instance is None:
+        model_instance = Model(model_weights)
+    return model_instance

requirements.txt

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+anyio==3.5.0
+asgiref==3.5.0
+certifi==2021.10.8
+charset-normalizer==2.0.12
+click==8.0.4
+colorama==0.4.4
+fastapi==0.75.0
+filelock==3.6.0
+gunicorn==20.1.0
+h11==0.13.0
+huggingface-hub==0.4.0
+idna==3.3
+joblib==1.1.0
+numpy==1.22.3
+packaging==21.3
+pydantic==1.9.0
+pyparsing==3.0.7
+PyYAML==6.0
+regex==2022.3.15
+requests==2.27.1
+sacremoses==0.0.49
+sentencepiece==0.1.96
+six==1.16.0
+sniffio==1.2.0
+starlette==0.17.1
+tokenizers==0.11.6
+--find-links https://download.pytorch.org/whl/torch_stable.html
+torch==1.11.0+cpu
+tqdm==4.63.0
+transformers==4.17.0
+typing_extensions==4.1.1
+urllib3==1.26.8
+uvicorn==0.17.6