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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import torch
import spaces
def get_model_name(language):
"""Map language choice to the corresponding model."""
model_mapping = {
"English": "microsoft/Phi-3-mini-4k-instruct",
"Arabic": "ALLaM-AI/ALLaM-7B-Instruct-preview"
}
return model_mapping.get(language, "ALLaM-AI/ALLaM-7B-Instruct-preview") # Default to Arabic model
def load_model(model_name):
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map=device,
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
return_full_text=False,
max_new_tokens=500,
do_sample=False
)
return generator
@spaces.GPU
def generate_plant_info(plant_name, language):
model_name = get_model_name(language)
generator = load_model(model_name)
# Define prompt for the AI model
if language == "English":
prompt = (f"Provide detailed information about {plant_name}. "
f"Include its scientific name, growing conditions (light, water, soil type), "
f"common uses, and care tips.")
else:
prompt = (f"قدم معلومات مفصلة عن {plant_name}. "
f"اذكر اسمه العلمي، وظروف نموه (الضوء، الماء، نوع التربة)، "
f"استخداماته الشائعة، ونصائح العناية به.")
messages = [{"role": "user", "content": prompt}]
output = generator(messages)
return output[0]["generated_text"]
# Create Gradio Blocks interface
with gr.Blocks(theme="soft") as demo:
gr.Markdown("<h1 style='text-align: center; color: #2E8B57;'>🌿 AI Plant Guide - English & Arabic 🌿</h1>")
gr.Markdown("<p style='text-align: center; font-size: 18px;'>Enter a plant name, and AI will provide detailed information about it in English or Arabic.</p>")
# Language selection
language_selector = gr.Radio(["English", "Arabic"], label="🌍 Choose Language", value="English")
# Plant name input
plant_name_input = gr.Textbox(placeholder="Enter plant name (e.g., Lavender, Aloe Vera)...", label="Plant Name")
output_text = gr.Textbox(label="Plant Information", interactive=False)
# Example button functionality
example_plants = [
("Lavender", "English"),
("اللافندر", "Arabic"),
("Tulip", "English"),
("الصبار", "Arabic"),
]
def update_inputs(plant_name, language):
return plant_name, language
with gr.Row():
for name, lang in example_plants:
example_button = gr.Button(f"🌿 {name} ({lang})")
# Use lambda to pass the arguments directly
example_button.click(lambda plant_name=name, language=lang: update_inputs(plant_name, language),
outputs=[plant_name_input, language_selector])
classify_button = gr.Button("🔍 Get Plant Info", variant="primary")
classify_button.click(generate_plant_info, inputs=[plant_name_input, language_selector], outputs=output_text)
# Run the application
if __name__ == "__main__":
demo.launch()
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