Delete nlp2_0.py

nlp2_0.py

@@ -1,93 +0,0 @@
-from transformers import pipeline
-
-# Load the FLAN Alpaca Large model
-paraphraser = pipeline("text2text-generation", model="declare-lab/flan-alpaca-large")
-
-!pip install transformers gradio sentencepiece
-
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
-import gradio as gr
-
-# Load model and tokenizer
-model_name = "ramsrigouthamg/t5_paraphraser"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-
-# Define style prompts
-def generate_prompt(text, style):
-    style_prompts = {
-        "Formal": "Please rewrite the following text in a formal and professional tone:\n\n",
-        "Friendly": "Please rewrite the following text in a casual and friendly tone:\n\n",
-        "Poetic": "Rewrite the text in a poetic and metaphorical way, like a short verse:\n\n",
-        "Gen Z": "Rewrite this text using Gen Z slang, internet expressions, abbreviations, and emojis:\n\n"
-    }
-
-    # Default if style not found
-    base_prompt = style_prompts.get(style, "Rewrite the text:\n\n")
-    return base_prompt + text
-
-# Function to rewrite text in selected style
-def rewrite_text(text, style):
-    if not text.strip():
-        return "Please enter some text."
-
-    # Build the prompt based on the selected style
-    if style == "Gen Z":
-        prompt = f"Rewrite the following text in a funny Gen Z tone with slang, emojis, and internet expressions:\n\n{text}"
-    elif style == "Poetic":
-        prompt = f"Rewrite the following text in a poetic and artistic style:\n\n{text}"
-    elif style == "Formal":
-        prompt = f"Rewrite the following text in a formal, professional tone:\n\n{text}"
-    elif style == "Friendly":
-        prompt = f"Rewrite the following text in a friendly and conversational style:\n\n{text}"
-    else:
-        prompt = f"Rewrite the following text:\n\n{text}"
-
-    # Call the model (paraphraser)
-    response = paraphraser(prompt, max_length=100)[0]['generated_text']
-
-    # Optional: clean output (remove repeated prompt from response if needed)
-    return response.replace(prompt, "").strip()
-
-
-# Gradio UI
-import gradio as gr
-
-# Define the interface
-with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
-    gr.Markdown(
-        """
-        <h1 style="text-align: center;">📝 Rewrite My Text</h1>
-        <p style="text-align: center;">Transform your text into <b>fun, formal, poetic, or Gen Z</b> styles using AI! 🚀</p>
-        """,
-        elem_id="header",
-    )
-
-    with gr.Row():
-        with gr.Column(scale=1):
-            input_text = gr.Textbox(
-                label="Input Text",
-                placeholder="Enter your sentence here...",
-                lines=4
-            )
-            style = gr.Dropdown(
-                label="Choose Style",
-                choices=["Gen Z", "Formal", "Poetic", "Friendly"],
-                value="Gen Z"
-            )
-            submit_button = gr.Button("✨ Submit", variant="primary")
-            clear_button = gr.Button("🧹 Clear")
-
-        with gr.Column(scale=1):
-            output_text = gr.Textbox(
-                label="Rewritten Text",
-                placeholder="Your rewritten sentence will appear here...",
-                lines=4
-            )
-
-    # Button functionality
-    submit_button.click(fn=rewrite_text, inputs=[input_text, style], outputs=output_text)
-    clear_button.click(fn=lambda: ("", ""), inputs=[], outputs=[input_text, output_text])
-
-# Launch the app
-demo.launch(share=True)