app.py

import gradio as gr
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
import torch
import numpy as np

# Initialize models
try:
    # Text Generation with TinyLlama
    generator_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
    tokenizer = AutoTokenizer.from_pretrained(generator_name)
    generator_model = AutoModelForCausalLM.from_pretrained(
        generator_name,
        torch_dtype=torch.float16,
        device_map="auto"
    )
    
    # Text Summarization
    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
    
    # Sentiment Analysis
    sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
    
    # Question Answering
    qa_model = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
    
    # Translation (English to multiple languages)
    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ROMANCE")
    
except Exception as e:
    print(f"Error loading models: {str(e)}")

def generate_text(prompt, max_length=100, temperature=0.7):
    """Generate text based on a prompt using TinyLlama"""
    try:
        # Format the prompt for chat
        formatted_prompt = f"<human>: {prompt}\n<assistant>:"
        
        # Generate text
        inputs = tokenizer(formatted_prompt, return_tensors="pt").to(generator_model.device)
        outputs = generator_model.generate(
            **inputs,
            max_length=max_length,
            temperature=temperature,
            do_sample=True,
            top_p=0.95,
            top_k=50,
            repetition_penalty=1.2,
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id
        )
        
        # Decode and clean up the response
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        # Remove the original prompt and clean up
        response = generated_text.split("<assistant>:")[-1].strip()
        return response
    except Exception as e:
        return f"Error in text generation: {str(e)}"

def summarize_text(text, max_length=130, min_length=30):
    """Summarize long text"""
    try:
        summary = summarizer(
            text,
            max_length=max_length,
            min_length=min_length,
            do_sample=False
        )
        return summary[0]['summary_text']
    except Exception as e:
        return f"Error in summarization: {str(e)}"

def analyze_sentiment(text):
    """Analyze sentiment of text"""
    try:
        result = sentiment_analyzer(text)
        return {
            "Sentiment": result[0]['label'],
            "Confidence": f"{result[0]['score']:.2%}"
        }
    except Exception as e:
        return {"error": str(e)}

def answer_question(context, question):
    """Answer questions based on context"""
    try:
        result = qa_model(
            question=question,
            context=context
        )
        return {
            "Answer": result['answer'],
            "Confidence": f"{result['score']:.2%}"
        }
    except Exception as e:
        return {"error": str(e)}

def translate_text(text, target_lang):
    """Translate text to target language"""
    try:
        translation = translator(
            text,
            src_lang="en",
            tgt_lang=target_lang
        )
        return translation[0]['translation_text']
    except Exception as e:
        return f"Error in translation: {str(e)}"

# Create the Gradio interface
with gr.Blocks(title="Advanced NLP") as demo:
    gr.Markdown("""
    # 🤖 Advanced NLP 
    ## Multi-task Language Model Application
    
    This application demonstrates various Natural Language Processing capabilities:
    - Text Generation (TinyLlama)
    - Text Summarization (BART)
    - Sentiment Analysis (DistilBERT)
    - Question Answering
    - Multi-language Translation
    
    Try out different tasks using the options below!
    """)
    
    with gr.Tab("Text Generation"):
        with gr.Row():
            with gr.Column():
                gen_input = gr.Textbox(label="Enter your prompt", lines=3)
                gen_length = gr.Slider(minimum=10, maximum=200, value=100, step=10, label="Maximum Length")
                gen_temp = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
                gen_button = gr.Button("Generate")
            with gr.Column():
                gen_output = gr.Textbox(label="Generated Text", lines=5)
    
    with gr.Tab("Text Summarization"):
        with gr.Row():
            with gr.Column():
                sum_input = gr.Textbox(label="Enter text to summarize", lines=8)
                sum_max_length = gr.Slider(minimum=50, maximum=200, value=130, step=10, label="Maximum Summary Length")
                sum_min_length = gr.Slider(minimum=10, maximum=100, value=30, step=5, label="Minimum Summary Length")
                sum_button = gr.Button("Summarize")
            with gr.Column():
                sum_output = gr.Textbox(label="Summary", lines=4)
    
    with gr.Tab("Sentiment Analysis"):
        with gr.Row():
            with gr.Column():
                sent_input = gr.Textbox(label="Enter text for sentiment analysis", lines=3)
                sent_button = gr.Button("Analyze Sentiment")
            with gr.Column():
                sent_output = gr.JSON(label="Sentiment Analysis Results")
    
    with gr.Tab("Question Answering"):
        with gr.Row():
            with gr.Column():
                qa_context = gr.Textbox(label="Enter the context", lines=6)
                qa_question = gr.Textbox(label="Enter your question", lines=2)
                qa_button = gr.Button("Get Answer")
            with gr.Column():
                qa_output = gr.JSON(label="Answer")
    
    with gr.Tab("Translation"):
        with gr.Row():
            with gr.Column():
                trans_input = gr.Textbox(label="Enter text to translate (English)", lines=3)
                trans_lang = gr.Dropdown(
                    choices=["es", "fr", "it", "pt", "ro"],
                    value="es",
                    label="Target Language"
                )
                trans_button = gr.Button("Translate")
            with gr.Column():
                trans_output = gr.Textbox(label="Translated Text", lines=3)
    
    # Set up event handlers
    gen_button.click(
        fn=generate_text,
        inputs=[gen_input, gen_length, gen_temp],
        outputs=gen_output
    )
    
    sum_button.click(
        fn=summarize_text,
        inputs=[sum_input, sum_max_length, sum_min_length],
        outputs=sum_output
    )
    
    sent_button.click(
        fn=analyze_sentiment,
        inputs=sent_input,
        outputs=sent_output
    )
    
    qa_button.click(
        fn=answer_question,
        inputs=[qa_context, qa_question],
        outputs=qa_output
    )
    
    trans_button.click(
        fn=translate_text,
        inputs=[trans_input, trans_lang],
        outputs=trans_output
    )

if __name__ == "__main__":
    demo.launch(share=True)