Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# Load a pre-trained model and tokenizer
+model_name = "t5-small"  # You can experiment with larger models like "t5-base" or "t5-large"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+# Create a pipeline for question-answering
+tutor_pipeline = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
+
+# Function to interact with the Tutor AI
+def tutor_ai(question):
+    input_text = f"explain: {question}"
+    response = tutor_pipeline(input_text, max_length=200, num_return_sequences=1)
+    return response[0]['generated_text']
+
+# Example usage
+question = "What is the Pythagorean theorem?"
+answer = tutor_ai(question)
+print(f"Q: {question}\nA: {answer}")
+
+question = "How do you solve a quadratic equation?"
+answer = tutor_ai(question)
+print(f"Q: {question}\nA: {answer}")