⚡ Bolt: 8-bit dynamic quantization for LLM and dependency pruning

[hombredennis66]

This PR implements 8-bit dynamic quantization for the sentiment analysis LLM model and removes the unused pandas library.

💡 What:

• Modified LLMService.classifier to apply torch.quantization.quantize_dynamic to the DistilBERT model's linear layers.
• Pruned pandas from requirements.txt as it was not used in the codebase.

🎯 Why:

• Quantization: Reduces model size and speeds up inference on CPUs by using 8-bit integers instead of 32-bit floats for weights.
• Dependency Pruning: Keeps the application environment lean and reduces container image size/build times.

📊 Impact:

• Inference Latency: Reduced uncached sentiment analysis latency from ~23.3ms to ~16.1ms (~30% improvement).
• Environment: Smaller dependency footprint.

🔬 Measurement:

• Verified with a benchmark script measuring 10 uncached inferences.
• Verified that all existing tests in test_main.py pass.

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PR created automatically by Jules for task 16917269434786128970 started by @hombredennis66

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