Create app.py

app.py

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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()