Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+import torch
+import gradio as gr
+
+# Charger le tokenizer depuis Hugging Face Spaces
+tokenizer = AutoTokenizer.from_pretrained("Dofla/roberta_base")
+
+# Charger le modèle depuis Hugging Face Spaces
+model = AutoModelForQuestionAnswering.from_pretrained("Dofla/roberta_base")
+def answer_question(context, question):
+    inputs = tokenizer.encode_plus(question, context, return_tensors="pt", padding=True, truncation=True)
+    start_logits, end_logits = model(**inputs)
+    outputs = model(**inputs)
+    start_logits = outputs.start_logits
+    end_logits = outputs.end_logits
+
+
+    # Assurez-vous que les logits sont des tenseurs
+    start_index = torch.argmax(start_logits, dim=1).item()
+    end_index = torch.argmax(end_logits, dim=1).item() + 1
+    answer = tokenizer.decode(inputs["input_ids"][0][start_index:end_index])
+    return answer
+# Créer une interface Gradio pour l'inférence
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=[
+        gr.Textbox(lines=7, label="Contexte"),
+        gr.Textbox(lines=1, label="Question")
+    ],
+    outputs="text",
+    title="Question Answering with Fine-Tuned Model"
+)
+
+# Lancer l'interface
+iface.launch('share=True')
+
+