Create app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr
+
+# Load your model and tokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "ahmedbasemdev/LLama3.2-fine-tuned"  # Replace with your model name
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+def single_inference(question):
+    messages = []
+
+    messages.append({"role": "user", "content": question})
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        return_tensors="pt"
+    ).to(model.device)
+
+    terminators = [
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+
+    outputs = model.generate(
+        input_ids,
+        max_new_tokens=256,
+        eos_token_id=terminators,
+        do_sample=True,
+        temperature=0.2,
+    )
+    response = outputs[0][input_ids.shape[-1]:]
+    output = tokenizer.decode(response, skip_special_tokens=True)
+    return output
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=single_inference,  # Function to wrap
+    inputs=gr.Textbox(lines=2, placeholder="Ask a question..."),  # Input type
+    outputs=gr.Textbox(label="Response"),  # Output type
+    title="Chat with Your Model",  # App title
+    description="Enter a question, and the model will generate a response.",  # App description
+)
+
+# Launch the app
+if __name__ == "__main__":
+    interface.launch()