Create app2.py

app2.py

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+import gradio as gr
+from ctransformers import AutoModelForCausalLM
+from transformers import AutoTokenizer, pipeline
+import torch
+import re
+
+# Initialize the model
+model = AutoModelForCausalLM.from_pretrained("Detsutut/Igea-1B-v0.0.1-Q4_K_M-GGUF")
+tokenizer = AutoTokenizer.from_pretrained( "Detsutut/Igea-350M-v0.0.1")
+
+
+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)