Update app.py

app.py

@@ -1,15 +1,29 @@
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 
-def predict(question):
-    # Placeholder logic – you can replace this with a model or knowledge base
-    return f"Su'aashaada '{question}' wali jawaabteeda lama heli karo. Fadlan dib u fiiri."
+# Load the model from Hugging Face Hub
+MODEL_NAME = "tacab/somali-agriculture"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
 
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(lines=2, placeholder="Gali su'aal ku saabsan beeraha..."),
-    outputs="text",
-    title="Tacab Somali Agriculture Q&A",
-    description="Su'aalo iyo jawaabo ku saabsan waaxda beeraha ee Soomaaliya."
-)
+# Create text generation pipeline
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
-iface.launch()
+# Function to use in the Gradio interface
+def generate_text(prompt):
+    output = generator(
+        prompt,
+        do_sample=True,
+        max_length=200,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    return output[0]["generated_text"]
+
+# Build Gradio interface
+gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=3, label="Geli qoraalka/Prompt-ka"),
+    outputs=gr.Textbox(label="Natiijada/Generated Text"),
+    title="Tacab – Somali Agriculture Generator",
+    description="App-kan wuxuu adeegsadaa model-ka 'tacab/somali-agriculture' si uu u abuuro qoraal cusub oo la xiriira beeraha Soomaaliyeed."
+).launch()