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Create app.py
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app.py
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from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
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import gradio as gr
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import torch
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# Load WizardMath (adjust model size if needed)
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model_name = "WizardLM/WizardMath-7B-V1.1" # Smaller: "WizardLM/WizardMath-70B-V1.0"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16, # Reduce memory usage
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device_map="auto" # Auto-select GPU/CPU
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)
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def solve_math_problem(question):
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# Format input for WizardMath
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prompt = f"USER: Solve this math problem: {question}\nASSISTANT:"
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# Generate response
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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inputs.input_ids,
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max_new_tokens=256,
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pad_token_id=tokenizer.eos_token_id
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)
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# Decode and clean output
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
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answer = answer.split("ASSISTANT:")[-1].strip() # Extract the answer part
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return answer
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# Gradio Interface
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demo = gr.Interface(
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fn=solve_math_problem,
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inputs=gr.Textbox(lines=2, placeholder="Enter your math problem here..."),
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outputs=gr.Textbox(label="Solution"),
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title="🧙 WizardMath Problem Solver",
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examples=[
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["What is the integral of x^2 from 0 to 3?"],
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["Solve for x: 2x + 5 = 15"],
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["Calculate the area of a circle with radius 4."]
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],
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theme="soft" # Try "default" or "huggingface"
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)
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if __name__ == "__main__":
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demo.launch(server_port=7860, share=False) # Set share=True for public link
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