Delete app_old.py

app_old.py

@@ -1,62 +0,0 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-import torch
-import re
-
-# Initialize the model
-model_id = "Detsutut/Igea-350M-v0.0.1"
-
-model = AutoModelForCausalLM.from_pretrained(model_id, load_in_8bit=True, device_map='auto')
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-
-gen_pipeline = pipeline(
-    "text-generation",
-    model=model,
-    tokenizer=tokenizer
-)
-
-# Define the function to generate text
-def generate_text(input_text, max_new_tokens, temperature, top_p, split_output):
-    if split_output:
-        max_new_tokens=30
-        top_p=0.95
-    output = gen_pipeline(
-        input_text,
-        max_new_tokens=max_new_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        return_full_text = False
-    )
-    generated_text = output[0]['generated_text']
-    if split_output:
-        sentences = re.split('(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', generated_text)
-        if sentences:
-            generated_text = sentences[0]
-    return f"<span>{input_text}</span><b style='color: blue;'>{generated_text}</b>"
-
-# Create the Gradio interface
-input_text = gr.Textbox(lines=2, placeholder="Enter your text here...", label="Input Text")
-
-max_new_tokens = gr.Slider(minimum=1, maximum=200, value=30, step=1, label="Max New Tokens")
-temperature = gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Temperature")
-top_p = gr.Slider(minimum=0.0, maximum=1.0, value=0.95, step=0.01, label="Top-p")
-split_output = gr.Checkbox(label="Quick single-sentence output", value=True)
-
-with gr.Blocks(css="#outbox { border-radius: 8px !important; border: 1px solid #e5e7eb !important; padding: 8px !important; text-align:center !important;}") as iface:
-    gr.Markdown("# Igea Text Generation Interface ⚕️🩺")
-    gr.Markdown("⚠️ 🐢💬 This model runs on a **hardware-limited**, free-tier HuggingFace space, resulting in a **low output token throughput** (approx. 1 token/s)")
-    input_text.render()
-    with gr.Accordion("Advanced Options", open=False):
-        max_new_tokens.render()
-        temperature.render()
-        top_p.render()
-        split_output.render()
-    output = gr.HTML(label="Generated Text",elem_id="outbox")
-
-    btn = gr.Button("Generate")
-    btn.click(generate_text, [input_text, max_new_tokens, temperature, top_p, split_output], output)
-
-# Launch the interface
-if __name__ == "__main__":
-    iface.launch(inline=True)