Update app.py

app.py

@@ -1,97 +1,86 @@
+
+
 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")
+#pipelines
+qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
+classification_pipeline = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+translation_pipeline = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
+topic_classification_pipeline = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")  # Fine-tuned model for topic classification
+summarization_pipeline = pipeline("summarization", model="facebook/bart-large-cnn")
+
+#functions
 
-def load_classifier_model():
-    return pipeline("zero-shot-classification", model="MoritzLaurer/deberta-v3-base-zeroshot-v1.1-all-33")
+def answer_question(context, question):
+    return qa_pipeline(question=question, context=context)["answer"]
 
-def load_translator_model(target_language):
-    model_name = f"translation_en_to_{target_language}"
-    return pipeline("translation_en_to_nl", model=model_name)
+def classify_text(text, labels):
+    labels = labels.split(",")
+    results = classification_pipeline(text, candidate_labels=labels)
+    return {label: float(f"{prob:.4f}") for label, prob in zip(results["labels"], results["scores"])}
 
-def load_generator_model():
-    return pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B", tokenizer="EleutherAI/gpt-neo-2.7B")
+def translate_text(text):
+    return translation_pipeline(text)[0]['translation_text'] if text else "No translation available"
 
-def load_summarizer_model():
-    return pipeline("summarization", model="facebook/bart-large-cnn")
+def classify_topic(text):
+    results = topic_classification_pipeline(text)
+    return ", ".join([f"{result['label']}: {result['score']:.4f}" for result in results])
 
-# Define functions to process inputs
-def process_qa(context, question):
-    qa_model = load_qa_model()
-    return qa_model(context=context, question=question)["answer"]
+def summarize_text(text):
+    result = summarization_pipeline(text, max_length=60)
+    return result[0]['summary_text'] if result else "No summary available"
 
-def process_classifier(text, labels):
-    classifier_model = load_classifier_model()
-    return classifier_model(text, labels)["labels"][0]
+def multi_model_interaction(text):
+    
+    summary = summarize_text(text)
+    translated_summary = translate_text(summary)
 
-def process_translation(text, target_language):
-    translator_model = load_translator_model(target_language)
-    translation = translator_model(text)[0]["translation_text"]
-    return translation
+    return {
+        "Summary (English)": summary,
+        "Summary (French)": translated_summary,
+    }
 
-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()
+        with gr.Column():
+          gr.Markdown("### Question Answering")
+            context = gr.Textbox(label="Context")
+            question = gr.Textbox(label="Question")
+          answer_output = gr.Text(label="Answer")
+          gr.Button("Answer").click(answer_question, inputs=[context, question], outputs=answer_output)
+
+        with gr.Column():
+            gr.Markdown("### Zero-Shot Classification")
+            text_zsc = gr.Textbox(label="Text")
+            labels = gr.Textbox(label="Labels (comma separated)")
+            classification_result = gr.JSON(label="Classification Results")
+            gr.Button("Classify").click(classify_text, inputs=[text_zsc, labels], outputs=classification_result)
+
+        with gr.Column():
+            gr.Markdown("### Translation")
+            text_to_translate = gr.Textbox(label="Text")
+            translated_text = gr.Text(label="Translated Text")
+            gr.Button("Translate").click(translate_text, inputs=[text_to_translate], outputs=translated_text)
+
+        with gr.Column():
+            gr.Markdown("### Sentiment Analysis")
+            text_for_sentiment = gr.Textbox(label="Text for Sentiment Analysis")
+            sentiment_result = gr.Text(label="Sentiment")
+            gr.Button("Classify Sentiment").click(classify_topic, inputs=[text_for_sentiment], outputs=sentiment_result)
+
+        with gr.Column():
+            gr.Markdown("### Summarization")
+            text_to_summarize = gr.Textbox(label="Text")
+            summary = gr.Text(label="Summary")
+            gr.Button("Summarize").click(summarize_text, inputs=[text_to_summarize], outputs=summary)
+
+    with gr.Tab("Multi-Model"):
+        gr.Markdown("### Multi-Model")
+        input_text = gr.Textbox(label="Enter Text for Multi-Model Analysis")
+        multi_output = gr.Text(label="Results")
+        gr.Button("Process").click(multi_model_interaction, inputs=[input_text], outputs=multi_output)
+
+    demo.launch(share=True, debug=True)