Create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+# Load the model and tokenizer
+model_name = "Qwen/Qwen2-Math-1.5B-Instruct"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+).to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define a function for Gradio to handle user input
+def solve_math(prompt):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": prompt}
+    ]
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+    generation_config = GenerationConfig(
+        do_sample=False,  # For greedy decoding
+        max_new_tokens=512
+    )
+
+    generated_ids = model.generate(
+        **model_inputs,
+        generation_config=generation_config
+    )
+
+    # Remove the input tokens from the output
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    # Decode the generated output and return the result
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=solve_math,  # Function to call
+    inputs="text",  # Text input for the user prompt
+    outputs="text", # Text output for the model's response
+    title="Math Solver",  # App title
+    description="Provide a math problem and the model will solve it."
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()