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Dockerfile

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+
+# Base image
+FROM python:3.9-slim
+
+# Set environment variables
+ENV PYTHONUNBUFFERED=1
+ENV PYTHONDONTWRITEBYTECODE=1
+
+# Set working directory
+WORKDIR /app
+
+# Install dependencies
+COPY requirements.txt /app/
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy application code
+COPY . /app/
+
+# Expose the application port
+EXPOSE 5000
+
+# Run the application
+CMD ["python", "app.py"]

app.py

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+
+from flask import Flask, request, jsonify
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+app = Flask(__name__)
+
+# Load the model and tokenizer
+MODEL_NAME = "adityagofi/Finetunning-Gemma-2-MedicalChatbot"  # Replace with your model path
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    try:
+        # Parse input data
+        data = request.get_json()
+        input_text = data.get("input_text", "")
+        max_length = data.get("max_length", 50)
+        
+        # Generate text
+        inputs = tokenizer.encode(input_text, return_tensors="pt")
+        outputs = model.generate(inputs, max_length=max_length, num_return_sequences=1, 
+                                  pad_token_id=tokenizer.eos_token_id)
+
+        # Decode the generated text
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        return jsonify({"generated_text": generated_text})
+    
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

requirements.txt

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+
+flask
+torch
+transformers