Add zero-GPU Gradio demo

.hf/hf_space_config.json

@@ -0,0 +1,4 @@
+{
+  "size": "cpu-basic",
+  "python_file": "app.py"
+}

app.py

@@ -0,0 +1,55 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load your fine-tuned model from the Hub
+MODEL_ID = "tasal9/ZamAI-mT5-Pashto"
+generator = pipeline(
+    "text2text-generation", 
+    model=MODEL_ID, 
+    tokenizer=MODEL_ID,
+    device=-1  # CPU only
+)
+
+# Prompt template
+
+def generate_prompt(instruction, input_text=""):
+    if input_text:
+        return (
+            f"Below is an instruction that describes a task, paired with an input that provides further context. "
+            f"Write a response that appropriately completes the request.\n\n"
+            f"### Instruction:\n{instruction}\n\n"
+            f"### Input:\n{input_text}\n\n"
+            f"### Response:"
+        )
+    else:
+        return (
+            f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+            f"### Instruction:\n{instruction}\n\n"
+            f"### Response:"
+        )
+
+# Inference function
+def predict(instruction, input_text):
+    prompt = generate_prompt(instruction, input_text)
+    outputs = generator(
+        prompt,
+        max_length=256,
+        num_beams=5,
+        early_stopping=True
+    )
+    return outputs[0]["generated_text"]
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter instruction here...", label="Instruction"),
+        gr.inputs.Textbox(lines=2, placeholder="Enter optional input here...", label="Input")
+    ],
+    outputs=gr.outputs.Textbox(label="Response"),
+    title="ZamAI mT5 Pashto Demo",
+    description="A zero-GPU Gradio demo for the ZamAI-mT5-Pashto model."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+transformers>=4.42.4
+torch