Update app.py

app.py

@@ -533,7 +533,7 @@ def download_llm_output(content, filename):
     buffer.write(content.encode('utf-8'))
     buffer.seek(0)
     # Add a download button to Streamlit
-    st.download_button(label="Download LLM Output", data=buffer, file_name=filename, mime='text/plain', icon="✏️", use_container_width=True)
+    st.download_button(label="Download AI Output", data=buffer, file_name=filename, mime='text/plain', icon="✏️", use_container_width=True)
 
 def prepare_llm_input(student_metrics_df):
     # Convert the student metrics DataFrame to a string
@@ -551,11 +551,34 @@ Provide:
 """
     return llm_input
 
+    
+# def prompt_response_from_hf_llm(llm_input):
+#     # Generate the refined prompt using Hugging Face API
+#     response = client.chat.completions.create(
+#         model="meta-llama/Llama-3.1-70B-Instruct",
+#         messages=[
+#             {"role": "user", "content": llm_input}
+#         ],
+#         stream=True,
+#         temperature=0.5,
+#         max_tokens=1024,
+#         top_p=0.7
+#     )
+
 def prompt_response_from_hf_llm(llm_input):
+    # Define a system prompt to guide the model's responses
+    system_prompt = """
+    <Persona> An expert Implementation Specialist at Michigan's Multi-Tiered System of Support Technical Assistance Center (MiMTSS TA Center) with deep expertise in SWPBIS, SEL, Structured Literacy, Science of Reading, and family engagement practices.</Persona> 
+    <Task> Analyze educational data and provide evidence-based recommendations for improving student outcomes across multiple tiers of support, drawing from established frameworks in behavioral interventions, literacy instruction, and family engagement.</Task> 
+    <Context> Operating within Michigan's educational system to support schools in implementing multi-tiered support systems, with access to student metrics data and knowledge of state-specific educational requirements and MTSS frameworks. </Context> 
+    <Format> Deliver insights through clear, actionable recommendations supported by data analysis, incorporating technical expertise while maintaining accessibility for educators and administrators at various levels of MTSS implementation.</Format>
+    """
+    
     # Generate the refined prompt using Hugging Face API
     response = client.chat.completions.create(
         model="meta-llama/Llama-3.1-70B-Instruct",
         messages=[
+            {"role": "system", "content": system_prompt},  # Add system prompt here
             {"role": "user", "content": llm_input}
         ],
         stream=True,