Update app.py

app.py

@@ -1,3 +1,57 @@
+import gradio as gr
+import torch
+from transformers import BertForMaskedLM, BertTokenizer
+import asyncio
+
+# Modell und Tokenizer laden mit force_download=True
+model_name = "bert-base-uncased"
+model = BertForMaskedLM.from_pretrained(model_name, force_download=True)
+tokenizer = BertTokenizer.from_pretrained(model_name, force_download=True)
+
+# Inferenz-Funktion definieren
+def inference(input_text):
+    if "[MASK]" not in input_text:
+        return "Error: The input text must contain the [MASK] token."
+    
+    # Tokenisierung
+    inputs = tokenizer(input_text, return_tensors="pt")
+    mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
+
+    # Vorhersage
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+    # Wahrscheinlichsten Token für [MASK] finden
+    mask_token_logits = logits[0, mask_token_index, :]
+    top_token = torch.topk(mask_token_logits, 1, dim=1).indices[0].tolist()
+
+    # Vorhersage in den Text einfügen
+    predicted_token = tokenizer.decode(top_token)
+    result_text = input_text.replace("[MASK]", predicted_token, 1)
+    
+    return result_text
+
+# Gradio Interface definieren
+iface = gr.Interface(
+    fn=inference,
+    inputs="text",
+    outputs="text",
+    examples=[
+        ["The capital of France is [MASK]."],
+        ["The quick brown fox jumps over the [MASK] dog."]
+    ]
+)
+
+# Interface starten
+if __name__ == "__main__":
+    # Asynchronen Ereignisloop manuell erstellen und zuweisen
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    
+    iface.launch(server_port=7862)
+
+
 from transformers import pipeline
 unmasker = pipeline('fill-mask', model='bert-base-uncased')
 unmasker("Hello I'm a [MASK] model.")