Create chatbot.py

chatbot.py

@@ -0,0 +1,60 @@
+def chat_with_model():
+    print("Welcome to the Question Answering Chatbot! (type 'exit' to quit)")
+    while True:
+        question = input("You: ")
+        if question.lower() == 'exit':
+            print("Goodbye!")
+            break
+        
+        context = input("Context: ")
+        if context.lower() == 'exit':
+            print("Goodbye!")
+            break
+        
+        response = question_answerer(question=question, context=context)
+        answer = response['answer']
+        score = response['score']
+        
+        print(f"Bot: {answer} (confidence: {score:.2f})")
+
+# Save chat function code in a script
+with open('chatbot.py', 'w') as f:
+    f.write('''
+from transformers import AutoTokenizer, TFAutoModelForQuestionAnswering, pipeline
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("my_awesome_qa_model")
+model = TFAutoModelForQuestionAnswering.from_pretrained("my_awesome_qa_model")
+
+# Create a pipeline for question answering
+question_answerer = pipeline("question-answering", model=model, tokenizer=tokenizer)
+
+# Define the chat function
+def chat_with_model():
+    print("Welcome to the Question Answering Chatbot! (type 'exit' to quit)")
+    while True:
+        question = input("You: ")
+        if question.lower() == 'exit':
+            print("Goodbye!")
+            break
+        
+        context = input("Context: ")
+        if context.lower() == 'exit':
+            print("Goodbye!")
+            break
+        
+        response = question_answerer(question=question, context=context)
+        answer = response['answer']
+        score = response['score']
+        
+        print(f"Bot: {answer} (confidence: {score:.2f})")
+
+# Run the chat function
+if __name__ == "__main__":
+    chat_with_model()
+''')
+
+print("Chatbot script 'chatbot.py' has been created.")
+
+if __name__ == "__main__":
+    chat_with_model()