Create app.py

app.py

@@ -0,0 +1,37 @@
+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()