Upload agent_frontend.py

agent_frontend.py

@@ -0,0 +1,88 @@
+# streamlit_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\.]*)[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)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+    return tokenizer, model
+
+def run_deepseek(user_question):
+    tokenizer, model = load_deepseek()
+
+    solution_steps = """### Solution Format:
+1. **Identify Given Data**
+2. **Determine the Required Variable**
+3. **Apply Relevant Equations**
+4. **Substitute Values & Solve Step-by-Step**
+5. **Verify & Conclude**
+
+### 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").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)