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Update app.py
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app.py
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# streamlit_app.py
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import
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from
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import
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# streamlit_app.py
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# app.py
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import streamlit as st
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import re
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from sympy import symbols, integrate, exp, pi
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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st.set_page_config(page_title="AI Physics Solver", page_icon="π§ ")
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x, t = symbols("x t")
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def extract_integral(problem_text):
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match = re.search(r'(\d+)\*?[tx]\^(\d+)', problem_text)
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limits = re.findall(r'[tx]\s*=\s*([\d\.\\\w]+)', problem_text)
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exp_match = re.search(r'(\d+)e\^([\-\+]?\d+\.?\d*)[tx]', problem_text)
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if 'radioactive' in problem_text or 'half-life' in problem_text:
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decay_match = re.search(r'(\d+)\s*e\^\s*-\s*(\d+\.?\d*)[tx]', problem_text)
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if decay_match and len(limits) == 2:
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N0 = int(decay_match.group(1))
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lam = float(decay_match.group(2))
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lower, upper = map(lambda v: eval(v, {"pi": pi}), limits)
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expr = lam * N0 * exp(-lam * t)
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return f"Total decayed = {integrate(expr, (t, lower, upper)).evalf()} units."
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if match and len(limits) == 2:
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coefficient = int(match.group(1))
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exponent = int(match.group(2))
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lower_limit = eval(limits[0], {"pi": pi})
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upper_limit = eval(limits[1], {"pi": pi})
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expr = coefficient * x**exponent
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return f"Accumulated Quantity = {integrate(expr, (x, lower_limit, upper_limit))}"
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return "Could not parse the integral format."
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@st.cache_resource
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def load_deepseek():
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model_name = "deepseek-ai/deepseek-math-7b-base"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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if torch.cuda.is_available():
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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else:
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model = AutoModelForCausalLM.from_pretrained(model_name)
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return tokenizer, model
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def run_deepseek(user_question):
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tokenizer, model = load_deepseek()
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solution_steps = """
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### Solution:
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1. Understand the problem and extract known quantities.
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2. Apply relevant physical laws or mathematical formulas.
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3. Solve algebraically or numerically as required.
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4. Clearly present the final answer.
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### Final Answer Format:
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Final Answer: [VARIABLE] = [ANSWER] [UNIT]
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"""
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prompt = f"Q: Solve the following physics problem using rigorous mathematical reasoning. Do not skip any steps.\n\nProblem: {user_question}\n\n{solution_steps}\nA:"
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inputs = tokenizer(prompt, return_tensors="pt")
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# Move inputs to GPU if available
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if torch.cuda.is_available():
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inputs = inputs.to("cuda")
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=500,
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temperature=0.1,
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repetition_penalty=1.0,
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eos_token_id=tokenizer.eos_token_id,
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pad_token_id=tokenizer.eos_token_id
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)
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return tokenizer.decode(outputs[0], skip_special_tokens=True).split("A:")[-1].strip()
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# ---------------- UI Layout ----------------
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st.title("π§ AI Science Solver")
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task_type = st.selectbox("Choose Task Type", ["LLM Reasoning (DeepSeek)", "Symbolic Integration"])
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user_question = st.text_area("Enter your physics or math question below:")
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if st.button("Solve"):
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with st.spinner("Solving..."):
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if task_type == "LLM Reasoning (DeepSeek)":
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result = run_deepseek(user_question)
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else:
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result = extract_integral(user_question)
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st.subheader("π Answer")
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st.write(result)
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