Update app.py

app.py

@@ -16,38 +16,42 @@ st.markdown("""
     """, unsafe_allow_html=True)
 
 st.title("📐 SAAD AI ACADEMY")
-st.caption("Stable Local Mathematics Engine | SUST Logic System")
+st.caption("Stable Mathematics Engine | SUST Logic System")
 
-# 3. LOCAL MODEL LOADING (Slow start, but 100% stable)
+# 3. STABLE LOCAL LOADING
 @st.cache_resource
 def load_engine():
-    # We use the same model, but load it locally to avoid API errors
     model_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
+    # Loading locally to ensure 100% uptime and no API timeouts
     return pipeline("text-generation", model=model_id, device=-1, torch_dtype=torch.float32)
 
-with st.spinner("⏳ Loading Math Engine into memory... This takes 1-2 minutes on first run."):
+with st.spinner("⏳ Restoring the Stable Engine..."):
     pipe = load_engine()
 
-# 4. UNIVERSAL LOGIC INSTRUCTIONS
+# 4. FIXED LOGIC INSTRUCTIONS
+# Added a check so it doesn't give random math for greetings
 system_instruction = (
-    "You are a Senior Mathematics Professor. Solve every problem with absolute rigor.\n"
-    "1. State the mathematical domain.\n"
-    "2. Check all constraints/conditions.\n"
-    "3. Show every algebraic step using LaTeX ($...$).\n"
-    "4. Bold the final result."
+    "You are a helpful assistant and a Senior Mathematics Professor. "
+    "If the user greets you or asks a general question, respond normally. "
+    "ONLY if the user provides a math problem, follow these rules:\n"
+    "1. State the math domain.\n"
+    "2. Show all steps using LaTeX ($...$).\n"
+    "3. Bold the final result."
 )
 
 # 5. INTERFACE
-user_query = st.chat_input("Ask a B.Sc. level math question...")
+user_query = st.chat_input("Enter your query...")
 
 if user_query:
     with st.chat_message("user"):
         st.write(user_query)
 
     with st.chat_message("assistant"):
-        with st.spinner("🧠 Thinking..."):
+        with st.spinner("🧠 Processing..."):
             prompt = f"<|im_start|>system\n{system_instruction}<|im_end|>\n<|im_start|>user\n{user_query}<|im_end|>\n<|im_start|>assistant\n"
-            result = pipe(prompt, max_new_tokens=800, temperature=0.1, do_sample=True)
+            
+            # Fixed generation settings to prevent hallucinations
+            result = pipe(prompt, max_new_tokens=800, temperature=0.7, do_sample=True)
             response = result[0]['generated_text'].split("<|im_start|>assistant\n")[-1]
             st.markdown(response)