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app.py

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+import gradio as gr
+from transformers import pipeline, Conversation
+
+# Models
+models = {
+    "text_generator": {"model": "EleutherAI/gpt-neo-1.3B", "task": "text-generation"},
+    "sentiment": {"model": "cardiffnlp/twitter-roberta-base-sentiment-latest", "task": "sentiment-analysis"},
+    "dialogue": {"model": "microsoft/DialoGPT-medium", "task": "conversational"},
+    "summarizer": {"model": "sshleifer/distilbart-cnn-12-6", "task": "summarization"},
+    "ner": {"model": "dbmdz/bert-large-uncased-finetuned-conll03-english", "task": "ner"},
+}
+
+# Initialize pipelines
+pipelines = {}
+for key, info in models.items():
+    pipelines[key] = pipeline(info["task"], model=info["model"])
+
+def generate_text(prompt, max_length):
+    result = pipelines["text_generator"](prompt, max_length=int(max_length), num_return_sequences=1)
+    return result[0]['generated_text']
+
+def analyze_sentiment(text):
+    result = pipelines["sentiment"](text)
+    return [{"label": res['label'], "score": f"{res['score']:.2f}"} for res in result]
+
+def converse(message, state):
+    if state is None:
+        state = ""
+    result = pipelines["dialogue"](state + "<|endoftext|>" + message, pad_token_id=50256)
+    return result[0]['generated_text']
+
+def summarize_text(text, max_length, min_length):
+    result = pipelines["summarizer"](text, max_length=int(max_length), min_length=int(min_length), do_sample=False)
+    return result[0]['summary_text']
+
+def ner_analysis(text):
+    result = pipelines["ner"](text, aggregation_strategy="max")
+    formatted = [f"{res['entity_group']}: {res['word']} ({res['score']:.2f})" for res in result]
+    return "\n".join(formatted)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# AI Art Main Interface")
+    gr.Markdown("Use various AI models below")
+
+    with gr.Tab("Text Generator"):
+        gr.Markdown("## Text Generator (EleutherAI/gpt-neo-1.3B)")
+        gen_prompt = gr.Textbox(label="Prompt")
+        gen_len = gr.Slider(10, 200, 50, label="Max Length")
+        gen_btn = gr.Button()
+        gen_out = gr.Textbox(label="Output")
+        gen_btn.click(generate_text, [gen_prompt, gen_len], gen_out)
+
+    with gr.Tab("Sentiment Analysis"):
+        gr.Markdown("## Sentiment Analyzer (cardiffnlp/twitter-roberta-base-sentiment-latest)")
+        sent_text = gr.Textbox(label="Text")
+        sent_btn = gr.Button()
+        sent_out = gr.JSON(label="Result")
+        sent_btn.click(analyze_sentiment, sent_text, sent_out)
+
+    with gr.Tab("Dialogue Bot"):
+        gr.Markdown("## Dialogue Bot (microsoft/DialoGPT-medium)")
+        chat_state = gr.State()
+        chat_msg = gr.Textbox(label="Message")
+        chat_send = gr.Button()
+        chat_out = gr.Chatbot()
+        chat_send.click(converse, [chat_msg, chat_state], [chat_out, chat_state])
+
+    with gr.Tab("Text Summarizer"):
+        gr.Markdown("## Text Summarizer (sshleifer/distilbart-cnn-12-6)")
+        sum_text = gr.Textbox(lines=5, label="Text")
+        sum_max = gr.Slider(10, 200, 100, label="Max Length")
+        sum_min = gr.Slider(5, 50, 10, label="Min Length")
+        sum_btn = gr.Button()
+        sum_out = gr.Textbox(label="Summary")
+        sum_btn.click(summarize_text, [sum_text, sum_max, sum_min], sum_out)
+
+    with gr.Tab("Named Entity Recognition"):
+        gr.Markdown("## Named Entity Recognition (dbmdz/bert-large-uncased-finetuned-conll03-english)")
+        ner_text = gr.Textbox(lines=3, label="Text")
+        ner_btn = gr.Button()
+        ner_out = gr.Textbox(lines=5, label="Entities")
+        ner_btn.click(ner_analysis, ner_text, ner_out)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch