Create app.py

app.py

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+from transformers import T5ForConditionalGeneration, T5Tokenizer
+import torch
+
+# Load the model and tokenizer
+t5ag_model = T5ForConditionalGeneration.from_pretrained("miiiciiii/I-Comprehend_ag")
+t5ag_tokenizer = T5Tokenizer.from_pretrained("miiiciiii/I-Comprehend_ag")
+
+def answer_question(question, context):
+    """Generate an answer for a given question and context."""
+    input_text = f"question: {question} context: {context}"
+    input_ids = t5ag_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True)
+    
+    with torch.no_grad():
+        output = t5ag_model.generate(input_ids, max_length=512, num_return_sequences=1, max_new_tokens=200)
+    
+    return t5ag_tokenizer.decode(output[0], skip_special_tokens=True)
+
+# Example usage
+question = "What is the location of the Eiffel Tower?"
+context = "The Eiffel Tower is located in Paris and is one of the most famous landmarks in the world."
+answer = answer_question(question, context)
+print("Generated Answer:", answer)