add deberta and squeezebert to the fastapi app and the modeles file

app.py

@@ -7,7 +7,7 @@ from fastapi import FastAPI, UploadFile, File
 from typing import Union 
 import json 
 import csv
-from modeles import bert
+from modeles import bert, squeezebert, deberta
 
 
 app = FastAPI()
@@ -48,11 +48,25 @@ async def create_upload_file(texte: str, model: str):
 
     return {"model": model, "texte": texte}
 
+# # Modèle Pydantic pour les requêtes SqueezeBERT
+# class SqueezeBERTRequest(BaseModel):
+#     context: str
+#     question: str
+@app.post("/squeezebert/")
+async def qasqueezebert(context: str, question: str):
+    try:
+        squeezebert_answer = squeezebert(context, question)
+        if squeezebert_answer:
+            return squeezebert_answer
+        else:
+            raise HTTPException(status_code=404, detail="No answer found")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
 # # Modèle Pydantic pour les requêtes BERT
 # class BERTRequest(BaseModel):
 #     context: str
 #     question: str
-
 @app.post("/bert/")
 async def qabert(context: str, question: str):
     try:
@@ -64,6 +78,20 @@ async def qabert(context: str, question: str):
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
 
+# # Modèle Pydantic pour les requêtes DeBERTa
+# class DeBERTaRequest(BaseModel):
+#     context: str
+#     question: str
+@app.post("/deberta-v2/")
+async def qadeberta(context: str, question: str):
+    try:
+        deberta_answer = deberta(context, question)
+        if deberta_answer:
+            return deberta_answer
+        else:
+            raise HTTPException(status_code=404, detail="No answer found")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
 
 def extract_data(file: UploadFile) -> Union[str, dict, list]:
     if file.filename.endswith(".txt"):

modeles.py

@@ -1,6 +1,41 @@
-# transformers obtenu via pip install automatique spécifié dans requirements.txt
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import torch
+
+def load_and_answer(question, context, model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+    # Tokenize the input question-context pair
+    inputs = tokenizer.encode_plus(question, context, max_length=512)
+
+    # Send inputs to the same device as your model
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        # Forward pass, get model outputs
+        outputs = model(**inputs)
+
+    # Extract the start and end positions of the answer in the tokens
+    answer_start_scores, answer_end_scores = outputs.start_logits, outputs.end_logits
+    answer_start_index = torch.argmax(answer_start_scores)  # Most likely start of answer
+    answer_end_index = torch.argmax(answer_end_scores) + 1  # Most likely end of answer; +1 for inclusive slicing
+
+    # Convert token indices to the actual answer text
+    answer_tokens = inputs['input_ids'][0, answer_start_index:answer_end_index]
+    answer = tokenizer.decode(answer_tokens, skip_special_tokens=True)
+    return {"answer": answer, "start": answer_start_index.item(), "end": answer_end_index.item()}
+
+def squeezebert(context, question):
+    # Define the specific model and tokenizer for SqueezeBERT
+    model_name = "ALOQAS/squeezebert-uncased-finetuned-squad-v2"
+    return load_and_answer(question, context, model_name)
 
 def bert(context, question):
-    question_answerer = pipeline("question-answering",  "alexandre-huynh/bert-base-uncased-finetuned-squad-single-epoch", framework="tf")
-    return(question_answerer(context=context, question=question))
+    # Define the specific model and tokenizer for BERT
+    model_name = "ALOQAS/bert-large-uncased-finetuned-squad-v2"
+    return load_and_answer(question, context, model_name)
+
+def deberta(context, question):
+    # Define the specific model and tokenizer for DeBERTa
+    model_name = "ALOQAS/deberta-large-finetuned-squad-v2"
+    return load_and_answer(question, context, model_name)