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app.py

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+
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model
+model_id = "htigenai/finetune_test"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        temperature=0.7,
+        top_p=0.95,
+        do_sample=True
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Create the interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs=gr.Textbox(),
+    title="Text Generation",
+    description="Generate text using the fine-tuned model"
+)
+
+iface.launch()