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import torch |
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from transformers import DistilBertTokenizerFast, DistilBertForQuestionAnswering |
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model_name = "distilbert-base-cased" |
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tokenizer = DistilBertTokenizerFast.from_pretrained(model_name) |
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model = DistilBertForQuestionAnswering.from_pretrained(model_name) |
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def format_response(start_index, end_index, raw_answer): |
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answer_tokens = tokenizer.convert_tokens_to_string([tokenizer.convert_ids_to_tokens(i)[0] for i in range(start_index, end_index+1)]) |
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return answer_tokens.strip() |
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def get_answers(question, context): |
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inputs = tokenizer.encode_plus(question, context, return_tensors="pt") |
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start_scores, end_scores = model(**inputs).values() |
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start_index = torch.argmax(start_scores) |
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end_index = torch.argmax(end_scores) + 1 |
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formatted_answer = format_response(start_index, end_index - 1, context[start_index:end_index].tolist()) |
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return formatted_answer |
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def main(): |
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print("Hi! I am a simple AI chatbot built using Hugging Face.") |
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print("Type 'quit' to exit the program.") |
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while True: |
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query = input("Your Question: ") |
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if query.lower() == "quit": |
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break |
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else: |
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if len(query) > 0: |
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context = "The capital of France is Paris." |
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try: |
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response = get_answers(query, context) |
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print(f"\nResponse: {response}\n") |
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except Exception as e: |
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print(f"\nError occurred: {str(e)}\n") |
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if __name__ == "__main__": |
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main() |
