Upload 3 files

Dockerfile

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+FROM python:3.9
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the requirements file into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install the dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the rest of the application code
+COPY . .
+
+# Expose port 7860 (required by Hugging Face Spaces)
+EXPOSE 7860
+
+# Run the application
+CMD ["python", "app.py"]

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import torch
+from transformers import BertTokenizerFast, BertForSequenceClassification
+import os
+
+app = Flask(__name__)
+CORS(app)
+
+# Set the device
+device = "cpu" # HF Spaces free tier is CPU only usually, or we can check.
+# But for safety and simplicity on free tier, let's stick to CPU or auto-detect but expect CPU.
+if torch.cuda.is_available():
+    device = "cuda"
+elif torch.backends.mps.is_available():
+    device = "mps"
+else:
+    device = "cpu"
+
+print(f"Using device: {device}")
+
+# Load the saved model and tokenizer
+# In HF Spaces, we will upload the model files to the root of the repository or a subfolder.
+# Let's assume the user uploads the contents of 'saved_bert_sentiment_model3final' to the root of the Space.
+# So the path is just "." or specific folder if they upload the folder itself.
+# To be safe and organized, let's assume they upload the folder 'model' or just files to root.
+# Best practice for manual upload: Upload files to root.
+MODEL_PATH = "." 
+
+try:
+    print("Loading model...")
+    loaded_tokenizer = BertTokenizerFast.from_pretrained(MODEL_PATH)
+    loaded_model = BertForSequenceClassification.from_pretrained(MODEL_PATH)
+    loaded_model.to(device)
+    loaded_model.eval()
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f"Error loading model: {e}")
+    # In a Space, if model fails, the app might crash or just not work.
+
+@app.route('/')
+def home():
+    return "Sentiment Analysis API is Running!"
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    data = request.json
+    text = data.get('text', '')
+    
+    if not text:
+        return jsonify({'error': 'No text provided'}), 400
+
+    try:
+        tokens = loaded_tokenizer(
+            text,
+            padding="max_length",
+            truncation=True,
+            max_length=128,
+            return_tensors="pt"
+        )
+        input_ids = tokens["input_ids"].to(device)
+        attention_mask = tokens["attention_mask"].to(device)
+
+        with torch.no_grad():
+            logits = loaded_model(input_ids, attention_mask=attention_mask).logits
+        
+        prediction = torch.argmax(logits, dim=1).item()
+        # 1 is Positive, 0 is Negative based on the notebook
+        sentiment = "Positive" if prediction == 1 else "Negative"
+        
+        # Calculate confidence (softmax)
+        probs = torch.nn.functional.softmax(logits, dim=1)
+        confidence = probs[0][prediction].item()
+
+        return jsonify({
+            'sentiment': sentiment,
+            'confidence': confidence,
+            'label': prediction
+        })
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    # HF Spaces expects the app to run on port 7860
+    app.run(debug=False, host='0.0.0.0', port=7860)

requirements.txt

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+flask
+flask-cors
+transformers
+torch