Update app.py

app.py

@@ -2,7 +2,7 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from sentence_transformers import SentenceTransformer, util
 from transformers import pipeline
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+#from transformers import T5Tokenizer, T5ForConditionalGeneration
 
 
 # Initialize FastAPI app
@@ -13,8 +13,9 @@ model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 question_model = "deepset/tinyroberta-squad2"
 nlp = pipeline('question-answering', model=question_model, tokenizer=question_model)
 
-t5tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
-t5model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+#t5tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-large")
+#t5model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 # Define request models
 class ModifyQueryRequest(BaseModel):
@@ -26,7 +27,6 @@ class AnswerQuestionRequest(BaseModel):
     locations: list
 
 class T5QuestionRequest(BaseModel):
-    question: str
     context: str
 
 class T5Response(BaseModel):
@@ -77,11 +77,8 @@ async def answer_question(request: AnswerQuestionRequest):
 
 @app.post("/t5answer", response_model=T5Response)
 async def t5answer(request: T5QuestionRequest):
-    input_text = request.question + ":" + request.context
-    input_ids = t5tokenizer(input_text, return_tensors="pt").input_ids
-    outputs = t5model.generate(input_ids)
-    resp = t5tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return T5Response(answer = resp)
+    resp = summarizer(request.context, max_length=130, min_length=30, do_sample=False)
+    return T5Response(answer = resp[0]["summary_text"])
 
 if __name__ == "__main__":
     import uvicorn