Create app.py

app.py

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+!pip install transformers datasets evaluate seqeval pipeline gradio typing-extensions
+import datasets
+from transformers import pipeline
+from transformers.pipelines.pt_utils import KeyDataset
+from tqdm.auto import tqdm
+
+pipe = pipe = pipeline("token-classification", model="erdometo/xlm-roberta-base-finetuned-TQuad2")
+dataset = datasets.load_dataset("superb", name="asr", split="test")
+
+for out in tqdm(pipe(KeyDataset(dataset, "file"))):
+    print(out)
+
+    
+
+import gradio as gr
+from transformers import pipeline, AutoModelForQuestionAnswering, AutoTokenizer, AutoModelForTokenClassification
+
+# Load your custom model and tokenizer
+qa_model_name = "erdometo/xlm-roberta-base-finetuned-TQuad2"
+token_classification_model_name = "FacebookAI/xlm-roberta-large-finetuned-conll03-german"
+
+qa_model = AutoModelForQuestionAnswering.from_pretrained(qa_model_name)
+qa_tokenizer = AutoTokenizer.from_pretrained(qa_model_name)
+
+token_classification_model = AutoModelForTokenClassification.from_pretrained(token_classification_model_name)
+token_classification_tokenizer = AutoTokenizer.from_pretrained(token_classification_model_name)
+# Define a function for inference based on pipeline type
+def predict(pipeline_type, question, context):
+    if pipeline_type == "question-answering":
+        qa_pipeline = pipeline("question-answering", model=qa_model, tokenizer=qa_tokenizer)
+        result = qa_pipeline(question=question, context=context)
+        response = [(result['answer'], result['score'])]
+        return response
+    elif pipeline_type == "token-classification":
+        token_classification_pipeline = pipeline("token-classification", model=token_classification_model, tokenizer=token_classification_tokenizer)
+        result = token_classification_pipeline(context)
+        highlighted_text = {"text": context, "entities": result} 
+        return gr.HighlightedText(highlighted_text)
+
+# Create a Gradio Interface with dropdown and two text inputs
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Dropdown(choices=["question-answering", "token-classification"], label="Choose Pipeline"),
+        "text",
+        "text"
+    ],
+    outputs=gr.Highlight()
+)
+
+# Launch the interface
+iface.launch()