Updated input prompt.

src/streamlit_app.py

@@ -33,17 +33,13 @@ st.title("Math Bot")
 
 # ── Get answers via HuggingFace LLM ───────────────────────────────────────
 def get_llm_response(user_prompt):
-    """Use HF Inference API to extract city names from a natural-language prompt."""
+    """Use HF Inference API to get help with highschool math from a natural-language prompt."""
     client = InferenceClient(
         # model="mistralai/Mistral-7B-Instruct-v0.3",
         # model="mistralai/Mistral-7B-Instruct-v0.2",
         # model="nvidia/Gemma-4-26B-A4B-NVFP4",
         # model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
         model="meta-llama/Llama-3.3-70B-Instruct",
-        # token=st.secrets.get("HF_TOKEN", None),
-        # token=st.secrets.get("rainytrek010526001read", None),
-        # token=st.secrets.get("API_KEY", None),
-        # token=os.getenv("API_KEY"),
         token=os.getenv("mathbot110526read"),
         provider="fireworks-ai"
     )
@@ -56,16 +52,12 @@ def get_llm_response(user_prompt):
 
     messages = [
         {"role": "system", "content": system_prompt},
-        {"role": "user", "content": f"Extract all city names from this text:\n\n{user_prompt}"},
+        {"role": "user", "content": f"Provide help with this question:\n\n{user_prompt}, give step-by-step diretives, if possible."},
     ]
 
     response = client.chat_completion(messages=messages, max_tokens=256, temperature=0.1)
     result = response.choices[0].message.content.strip()
     
-    ## raw = [c.strip() for c in response["choices"][0]["message"]["content"].split("\"")  if len(c.strip()) > 1]
-    ## cities = json.loads(raw)
-    # return [c.strip() for c in cities if isinstance(c, str) and c.strip()]
-
     return result
 
 # ── Show response ────────────────────────────────────────────────
@@ -97,16 +89,9 @@ if run and user_input.strip():
     with st.spinner("Asking the LLM to help with math…"):
         try:
             ### get and handle response here
-            # reponse = get_llm_response(user_prompt=question_text)
             response = get_llm_response(user_prompt=user_input)
-
-            # show_response(question_text, response_text)
             show_response(user_input, response)
             
-            # cities = extract_cities_with_llm(user_input)
-            ### forecast = fetch_forecast(loc["lat"], loc["lon"], loc["timezone"])
-            ### render_city_card(loc, forecast)
-            
         except Exception as e:
             st.markdown(f'<div class="error-box">⚠️ LLM error: {e}</div>', unsafe_allow_html=True)
 
@@ -116,7 +101,7 @@ elif run:
 st.markdown("---")
 st.markdown(
     '<div style="font-family: Space Mono, monospace; font-size: 0.65rem; color: #37474f; text-align:center;">'
-    'Weather data: Open-Meteo (open-source, no API key needed) · LLM: Llama-3.3-70B-Instruct via Hugging Face Inference API'
+    'LLM: Llama-3.3-70B-Instruct via Hugging Face Inference API'
     '</div>',
     unsafe_allow_html=True,
 )