Create app.py

app.py

@@ -0,0 +1,43 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load models
+gen_model = pipeline("text2text-generation", model="google/flan-t5-large")
+translator_en_ar = pipeline("translation", model="Helsinki-NLP/opus-mt-en-ar")  # English to Arabic
+translator_ar_en = pipeline("translation", model="Helsinki-NLP/opus-mt-ar-en")  # Arabic to English
+
+def get_plant_info(plant_name, language):
+    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 how to take care of it."
+        )
+        response = gen_model(prompt, min_length=50, max_length=300)[0]["generated_text"]
+    else:  # Arabic
+        translated_name = translator_ar_en(plant_name)[0]["translation_text"]  # Convert Arabic input to English
+        prompt = (
+            f"Provide detailed information about {translated_name}. "
+            f"Include its scientific name, growing conditions (light, water, soil type), "
+            f"common uses, and how to take care of it."
+        )
+        response_en = gen_model(prompt, min_length=50, max_length=300)[0]["generated_text"]
+        response = translator_en_ar(response_en)[0]["translation_text"]  # Convert English output back to Arabic
+
+    return response
+
+# Gradio UI
+interface = gr.Interface(
+    fn=get_plant_info,
+    inputs=[
+        gr.Textbox(label="Enter Plant Name / أدخل اسم النبات"),
+        gr.Radio(["English", "العربية"], label="Choose Language / اختر اللغة")
+    ],
+    outputs=gr.Textbox(label="Plant Information / معلومات النبات", lines=10),
+    title="Plant Information App",
+    description="Enter a plant name and select a language to get detailed information."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    demo.launch()