Create generate_answer.py

generate_answer.py

@@ -0,0 +1,17 @@
+from transformers import BartTokenizer, BartModel
+from langchain.prompts import PromptTemplate
+
+tokenizer = BartTokenizer.from_pretrained('facebook/bart-base')
+model = BartModel.from_pretrained('facebook/bart-base')
+
+def generate_answer(context):
+    prompt_template = PromptTemplate(template="Summarise the following context: {context}",
+                                     input_variables=["context"], output_variables=["answer"])
+    # Model loading
+    format_prompt = prompt_template.format(question=question)
+    encoded_input = tokenizer(format_prompt, return_tensors='pt')
+    # Run the model
+    output = model.generate(**encoded_input)  # Use generate method for text generation
+    # Decode the model output to text
+    decoded_output = tokenizer.decode(output[0])
+    return decoded_output