Delete main.py

main.py

@@ -1,37 +0,0 @@
-from transformers import DistilBertTokenizerFast, DistilBertForQuestionAnswering
-import json
-import streamlit as st
-
-model_name = "distilbert-base-cased"
-tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)
-model = DistilBertForQuestionAnswering.from_pretrained(model_name)
-
-def format_response(start_index, end_index, raw_answer):
-    answer_tokens = tokenizer.convert_tokens_to_string([tokenizer.convert_ids_to_tokens(i)[0] for i in range(start_index, end_index+1)])
-    return {'answer': answer_tokens.strip(), 'score': None}
-
-def get_answers(question, context):
-    inputs = tokenizer.encode_plus(question, context, return_tensors="pt")
-    start_scores, end_scores = model(**inputs).values()
-    start_index = torch.argmax(start_scores)
-    end_index = torch.argmax(end_scores) + 1
-    formatted_answer = format_response(start_index, end_index - 1, context[start_index:end_index].tolist())
-    return formatted_answer
-
-def interactive():
-    print("Hi! I am a simple AI chatbot built using Hugging Face.")
-    while True:
-        query = input("\nAsk me something or type 'quit' to exit:\n").lower().strip()
-        if query == "quit":
-            break
-        
-        try:
-            # Add some basic context here; replace with your own dataset later
-            context = "The capital of France is Paris."
-            response = get_answers(query, context)
-            print(f"\n{json.dumps(response)}")
-        except Exception as e:
-            print(f"Error occurred: {str(e)}")
-
-if __name__ == "__main__":
-    interactive()