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