Update app.py

app.py

@@ -1,36 +1,31 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
-import torch
-import warnings
-warnings.filterwarnings("ignore")
-
-# Load models
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-mul")
-speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
-question_generator_model = pipeline("text2text-generation", model="iarfmoose/t5-base-question-generator")
-
+from transformers import pipeline
 
+# Preload models
+summarizer = pipeline("summarization")
+sentiment_analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
+translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
+translator_fr = pipeline("Helsinki-NLP/opus-mt-en-fr")
+translator_de = pipeline("Helsinki-NLP/opus-mt-en-de")
+translator_es = pipeline("Helsinki-NLP/opus-mt-en-es")
+translator_ta = pipeline("translation", model="facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="tam_Taml")
+speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+question_generator = pipeline("e2e-qg")
 
-# Functions
+# --- Functional Modules ---
 
-def summarize_text(text):
-    summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
-    return summary[0]['summary_text']
+def summarize(text):
+    return summarizer(text, max_length=60, min_length=20, do_sample=False)[0]['summary_text']
 
 def analyze_sentiment(text):
     result = sentiment_analyzer(text)[0]
-    return f"{result['label']} (score: {result['score']:.2f})"
-
-from transformers import pipeline
-
-# Updated Translation Pipelines
-translator_ta = pipeline("translation", model="facebook/nllb-200-distilled-600M", src_lang="eng_Latn", tgt_lang="tam_Taml")
-translator_hi = pipeline("translation", model="Helsinki-NLP/opus-mt-en-hi")
-translator_fr = pipeline("translation", model="Helsinki-NLP/opus-mt-en-fr")
-translator_de = pipeline("translation", model="Helsinki-NLP/opus-mt-en-de")
-translator_es = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")
+    label = result["label"]
+    if label == "LABEL_1":
+        return "Neutral"
+    elif label == "LABEL_2":
+        return "Positive"
+    else:
+        return "Negative"
 
 def translate(text, lang):
     if lang == "Tamil":
@@ -44,54 +39,85 @@ def translate(text, lang):
     elif lang == "Spanish":
         return translator_es(text)[0]["translation_text"]
     else:
-        return "Unsupported language."
-
-
-
-def transcribe_audio(audio):
-    result = speech_to_text(audio)
-    return result['text']
-
-def generate_questions(paragraph):
-    qg_input = "generate questions: " + paragraph
-    questions = question_generator_model(qg_input, max_length=256, do_sample=False)[0]['generated_text']
-    return questions
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# 🌐 Multi-Task AI App (Hugging Face Space)\n**By Joel** — Powered by Transformers")
-
-    with gr.Tab("Text Summarization"):
-        input_text = gr.Textbox(label="Enter a long paragraph")
-        summary_output = gr.Textbox(label="Summarized text")
-        summarize_btn = gr.Button("Summarize")
-        summarize_btn.click(summarize_text, input_text, summary_output)
-
-    with gr.Tab("Sentiment Analysis"):
-        sentiment_input = gr.Textbox(label="Enter a sentence")
-        sentiment_output = gr.Textbox(label="Sentiment Result")
-        sentiment_btn = gr.Button("Analyze Sentiment")
-        sentiment_btn.click(analyze_sentiment, sentiment_input, sentiment_output)
-
-    with gr.Tab("Translation"):
-       translation_input = gr.Textbox(label="Enter English text")
-       target_lang = gr.Dropdown(choices=["Tamil", "Hindi", "French", "German", "Spanish"], label="Choose target language", value="Tamil")
-       translation_output = gr.Textbox(label="Translated text")
-       translate_btn = gr.Button("Translate")
-       translate_btn.click(fn=translate, inputs=[translation_input, target_lang], outputs=translation_output)
-
-
-    with gr.Tab("Speech-to-Text"):
-        audio_input = gr.Audio(label="Upload or record audio", type="filepath")
-        transcript_output = gr.Textbox(label="Transcribed Text")
-        transcribe_btn = gr.Button("Transcribe")
-        transcribe_btn.click(transcribe_audio, audio_input, transcript_output)
-
-    with gr.Tab("Question Generation"):
-        paragraph_input = gr.Textbox(label="Enter a paragraph to generate questions", lines=5)
-        question_output = gr.Textbox(label="Generated Questions")
-        question_btn = gr.Button("Generate Questions")
-        question_btn.click(generate_questions, paragraph_input, question_output)
+        return "Unsupported Language"
+
+def transcribe(audio):
+    return speech_to_text(audio)["text"]
+
+def generate_questions(text):
+    output = question_generator(text)
+    return "\n".join(f"- {item['question']}" for item in output[:10])
+
+# --- UI Sections for each task ---
+
+def summarization_ui():
+    with gr.Column():
+        input_text = gr.Textbox(label="Enter a long paragraph", lines=8, placeholder="Paste your paragraph here...")
+        output_text = gr.Textbox(label="Summarized text", lines=4)
+        gr.Button("Summarize").click(summarize, inputs=input_text, outputs=output_text)
+
+def sentiment_ui():
+    with gr.Column():
+        input_text = gr.Textbox(label="Enter a sentence", lines=3)
+        output_text = gr.Textbox(label="Sentiment")
+        gr.Button("Analyze Sentiment").click(analyze_sentiment, inputs=input_text, outputs=output_text)
+
+def translation_ui():
+    with gr.Column():
+        input_text = gr.Textbox(label="Enter English text", lines=3)
+        lang_dropdown = gr.Dropdown(["Tamil", "Hindi", "French", "German", "Spanish"], value="Tamil", label="Target Language")
+        output_text = gr.Textbox(label="Translated text", lines=3)
+        gr.Button("Translate").click(translate, inputs=[input_text, lang_dropdown], outputs=output_text)
+
+def speech_ui():
+    with gr.Column():
+        audio = gr.Audio(source="microphone", type="filepath", label="Record or Upload")
+        output_text = gr.Textbox(label="Recognized Text")
+        gr.Button("Convert Speech to Text").click(transcribe, inputs=audio, outputs=output_text)
+
+def question_ui():
+    with gr.Column():
+        input_text = gr.Textbox(label="Enter a paragraph", lines=8)
+        output_text = gr.Textbox(label="Generated Questions", lines=10)
+        gr.Button("Generate Questions").click(generate_questions, inputs=input_text, outputs=output_text)
+
+# --- Homepage Navigation ---
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("<h1 style='text-align:center;'>🌐 Multi-Task AI App (Hugging Face Space)</h1>")
+    gr.Markdown("<h4 style='text-align:center;'>By Joel — Powered by Transformers</h4>")
+    
+    with gr.Row():
+        btn1 = gr.Button("Text Summarization")
+        btn2 = gr.Button("Sentiment Analysis")
+        btn3 = gr.Button("Translation")
+        btn4 = gr.Button("Speech-to-Text")
+        btn5 = gr.Button("Question Generation")
+
+    main_content = gr.Column()
+
+    with main_content:
+        output_area = gr.Column(visible=False)
+
+    def load_tab(tab_name):
+        with output_area:
+            output_area.children.clear()
+            if tab_name == "summarization":
+                summarization_ui()
+            elif tab_name == "sentiment":
+                sentiment_ui()
+            elif tab_name == "translation":
+                translation_ui()
+            elif tab_name == "speech":
+                speech_ui()
+            elif tab_name == "question":
+                question_ui()
+            output_area.visible = True
+
+    btn1.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'summarization'")
+    btn2.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'sentiment'")
+    btn3.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'translation'")
+    btn4.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'speech'")
+    btn5.click(fn=load_tab, inputs=[], outputs=[], _js="() => 'question'")
 
-# Launch app
 demo.launch()