Update app.py

app.py

@@ -1,88 +1,97 @@
-# 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)
+# 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)