Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Lazy loading: Define functions to load models only when needed
+def load_qa_model():
+    return pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")
+
+def load_classifier_model():
+    return pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33")
+
+def load_translator_model(target_language):
+    model_name = f"translation_en_to_{target_language}"
+    return pipeline("translation_en_to_nl", model=model_name)
+
+def load_generator_model():
+    return pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B", tokenizer="EleutherAI/gpt-neo-2.7B")
+
+def load_summarizer_model():
+    return pipeline("summarization", model="facebook/bart-large-cnn")
+
+# Define functions to process inputs
+def process_qa(context, question):
+    qa_model = load_qa_model()
+    return qa_model(context=context, question=question)["answer"]
+
+def process_classifier(text, labels):
+    classifier_model = load_classifier_model()
+    return classifier_model(text, labels)["labels"][0]
+
+def process_translation(text, target_language):
+    translator_model = load_translator_model(target_language)
+    translation = translator_model(text)[0]["translation_text"]
+    return translation
+
+def process_generation(prompt):
+    generator_model = load_generator_model()
+    return generator_model(prompt, max_length=50)[0]["generated_text"]
+
+def process_summarization(text):
+    summarizer_model = load_summarizer_model()
+    return summarizer_model(text, max_length=150, min_length=40, do_sample=False)[0]["summary_text"]
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("Choose an NLP task and input the required text.")
+    
+    with gr.Tab("Single Models"):
+        gr.Markdown("This tab is for single models demonstration.")
+        # Single models interface
+        task_select_single = gr.Dropdown(["Question Answering", "Zero-Shot Classification", "Translation", "Text Generation", "Summarization"], label="Select Task")
+        input_fields_single = [gr.Textbox(label="Input")]
+        if task_select_single.value == "Zero-Shot Classification":
+            input_fields_single.append(gr.CheckboxGroup(["Label 1", "Label 2", "Label 3"], label="Labels"))
+        elif task_select_single.value == "Translation":
+            input_fields_single.append(gr.Dropdown(["nl", "fr", "es", "de"], label="Target Language"))
+        output_text_single = gr.Textbox(label="Output")
+
+        execute_button_single = gr.Button("Execute")
+
+        def execute_task_single():
+            task = task_select_single.value
+            inputs = [field.value for field in input_fields_single]
+            print("Inputs (Single Models):", inputs)
+            if task == "Translation":
+                translation = process_translation(*inputs)
+                print("Translation result (Single Models):", translation)
+                output_text_single.update(translation)
+            else:
+                output_text_single.update(eval(f"process_{task.lower()}")(*inputs))
+            print("Output updated (Single Models)")
+
+        execute_button_single.click(execute_task_single)
+    
+    with gr.Tab("Multi-model"):
+        gr.Markdown("This tab is for multi-model demonstration.")
+        # Multi-model interface
+        task_select_multi = gr.Dropdown(["Question Answering", "Zero-Shot Classification", "Translation", "Text Generation", "Summarization"], label="Select Task")
+        input_text_multi = gr.Textbox(label="Input")
+        output_text_multi = gr.Textbox(label="Output")
+
+        execute_button_multi = gr.Button("Execute")
+
+        def execute_task_multi():
+            task = task_select_multi.value
+            input_text = input_text_multi.value
+            print("Input (Multi-model):", input_text)
+            if task == "Translation":
+                translation = process_translation(input_text, "nl")  # Default to Dutch translation
+                print("Translation result (Multi-model):", translation)
+                output_text_multi.update(translation)
+            else:
+                output_text_multi.update(eval(f"process_{task.lower()}")(input_text))
+            print("Output updated (Multi-model)")
+
+        execute_button_multi.click(execute_task_multi)
+
+demo.launch()