Create app.py

app.py

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+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)