gradio app.py

app.py

@@ -0,0 +1,35 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model_name = "rkwsuper/lora_model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Define the function for inference
+def generate_text(prompt, max_length=100, temperature=1.0):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs["input_ids"],
+        max_length=max_length,
+        temperature=temperature,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Enter Prompt"),
+        gr.Slider(10, 300, value=100, step=10, label="Max Length"),
+        gr.Slider(0.1, 2.0, value=1.0, step=0.1, label="Temperature")
+    ],
+    outputs="text",
+    title="Hugging Face Model Text Generator",
+    description="This interface generates text based on your input using a fine-tuned Hugging Face model."
+)
+
+# Launch
+if __name__ == "__main__":
+    iface.launch()