changes

app.py

@@ -4,26 +4,76 @@ import torch
 from fastapi import FastAPI, UploadFile, File
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
+import torch.nn as nn
+import pickle
+import re
+
+token_2_id = None
+# Load the dictionary later
+with open(".\\vocab.pkl", "rb") as f:
+    token_2_id = pickle.load(f)
+print(token_2_id)
+
+
+def normalize(text):
+    text = text.lower()
+    text = re.sub(r'[^a-z0-9\s]', '', text)
+    text = ' '.join(text.split())
+    return text
+
+
+def tokenize(text):
+    tokens = text.split()
+    return tokens
+
+
+def convert_tokens_2_ids(tokens):
+    input_ids = [
+        token_2_id.get(token, token_2_id['<UNK>']) for token in tokens
+    ]
+    return input_ids
 
-app = FastAPI()
 
-# Load the pre-trained model from within the container
-model_uri = "model.pth"
-model = torch.load(model_uri, weights_only=False)
+def process_text(text, aspect):
+    text_aspect_pair = text + ' ' + aspect
+    normalized_text = normalize(text_aspect_pair)
+    tokens = tokenize(normalized_text)
+    input_ids = convert_tokens_2_ids(tokens)
+    input_ids = torch.tensor(input_ids).unsqueeze(0)
+    return input_ids
 
-# Define input schema for JSON requests
-class ImageInput(BaseModel):
-    image_path: str
 
+class ABSA(nn.Module):
+    def __init__(self, vocab_size, num_labels=3):
+        super(ABSA, self).__init__()
+        self.vocab_size = vocab_size
+        self.num_labels = num_labels
+        self.embedding_layer = nn.Embedding(
+            num_embeddings=vocab_size, embedding_dim=256
+        )
+        self.lstm_layer = nn.LSTM(
+            input_size=256,
+            hidden_size=512,
+            batch_first=True,
+        )
 
-# Preprocess the image
-def preprocess_image(image):
-    image = image.convert('L')  # Convert to grayscale
-    image = image.resize((28, 28))
-    image = np.array(image) / 255.0  # Normalize to [0, 1]
-    image = (image - 0.1307) / 0.3081  # Standardize
-    image = torch.tensor(image).unsqueeze(0).float()  # Convert to tensor with batch dimension
-    return image
+        self.fc_layer = nn.Linear(
+            in_features=512,
+            out_features=self.num_labels
+        )
+
+    def forward(self, x):
+        embeddings = self.embedding_layer(x)
+        lstm_out, _ = self.lstm_layer(embeddings)
+        logits = self.fc_layer(lstm_out[:, -1, :])
+        return logits
+
+
+model = ABSA(vocab_size=len(token_2_id.keys()), num_labels=3)
+model = model.load_state_dict(torch.load('model_weights.pth'))
+
+print("Model loaded successfully")
+app = FastAPI()
 
 
 # Root endpoint
@@ -31,21 +81,21 @@ def preprocess_image(image):
 def greet_json():
     return {"Hello": "World!"}
 
-
-# Predict endpoint for JSON input
-@app.post("/predict")
-async def predict_image(file: UploadFile = File(...)):
-    try:
-        # Read and preprocess the uploaded image
-        image = Image.open(file.file)
-        image = preprocess_image(image)
-
-        # Make prediction
-        model.eval()
-        with torch.no_grad():
-            output = model(image)
-            prediction = output.argmax(dim=1).item()
-
-        return JSONResponse(content={"prediction": f"The digit is {prediction}"})
-    except Exception as e:
-        return JSONResponse(content={"error": str(e)}, status_code=500)
+#
+# # Predict endpoint for JSON input
+# @app.post("/predict")
+# async def predict_image(file: UploadFile = File(...)):
+#     try:
+#         # Read and preprocess the uploaded image
+#         image = Image.open(file.file)
+#         image = preprocess_image(image)
+#
+#         # Make prediction
+#         model.eval()
+#         with torch.no_grad():
+#             output = model(image)
+#             prediction = output.argmax(dim=1).item()
+#
+#         return JSONResponse(content={"prediction": f"The digit is {prediction}"})
+#     except Exception as e:
+#         return JSONResponse(content={"error": str(e)}, status_code=500)

model_weights.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b1a6752d09f7a562dd93c6d857987c4df789908e132287bef226ac82c6c2a80
+size 10208198

model.pth → vocab.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:5a25d6fbe70a15a02f24ff1e586b44b4fb0a626193293b44fb718a18851b9f12
-size 445812
+oid sha256:2e195b47d3a84c730ff3a7f25df52b335174ba06914d9404bffa7f4422603d60
+size 47213