Update app.py

app.py

@@ -14,62 +14,56 @@ vector_store = Chroma(
     collection_name="geometry_sol"
 )
 
-# Load OpenAI key (you must add this in Hugging Face Space Secrets)
+# Load OpenAI key (set this in Hugging Face Space Secrets)
 os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
 
 # Load the LLM (GPT-3.5)
 llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.3)
 
-# Prompt templates
-templates = {
-    "general": PromptTemplate(
-        input_variables=["context", "query"],
-        template="""
-You are a strict assistant for the Virginia Geometry SOL.
+# Unified prompt to auto-detect intent
+template = PromptTemplate(
+    input_variables=["context", "query"],
+    template="""
+You are a Virginia high school Geometry assistant. Based on the user question below, determine the correct response type and answer accordingly:
 
-Only use exact phrases from the following SOL text:
-{context}
-
-Answer the question: "{query}"
-
-If the answer is in the SOL text, quote it exactly. Do not rephrase or summarize. Do not add your own explanation.
+User Question: 
+{query}
 
-If the answer is not in the context, reply: "The answer is not found in the provided SOL text."
-"""
-    ),
-    "lesson plan": PromptTemplate(
-        input_variables=["context", "query"],
-        template="""
-Given the following retrieved SOL text:
+Based on the following SOL text:
 {context}
 
-Generate a Geometry lesson plan based on: "{query}"
-Include:
-1. Simple explanation of the concept.
-2. Real-world example.
-3. Engaging class activity.
-Be concise and curriculum-aligned for high school.
+Response Rules:
+- If the question is asking for an **SOL number**, respond with:
+  1. The exact SOL code (e.g., G.RLT.1)
+  2. The exact description line from the SOL guide  
+  ⚠️ Do not summarize. Only copy directly from the context.
+
+- If the user asks for a **lesson plan**, provide:
+  - Simple explanation of the concept
+  - Real-world example
+  - Engaging class activity  
+  Format the output clearly with bullet points.
+
+- If the user asks for a **worksheet**, include:
+  - Concept summary
+  - A worked example
+  - 3 practice problems  
+  Format the output clearly with bullet points.
+
+- If the user asks for **proofs**, include:
+  - Student-friendly explanation
+  - Real-world connection
+  - One short class activity  
+  Format the output clearly with bullet points.
+
+- If the user asks for **flashcards**, generate 5 cards, each with:
+  - A clear question
+  - A short answer  
+  Format the output clearly with bullet points.
+
+Only answer one way depending on the intent of the question.
 """
-    ),
-    "worksheet": PromptTemplate(
-        input_variables=["context", "query"],
-        template="""
-{context}
-
-Create a student worksheet for: "{query}"
-Include: concept summary, a worked example, and 3 practice problems.
-"""
-    ),
-    "proofs": PromptTemplate(
-        input_variables=["context", "query"],
-        template="""
-{context}
-
-Generate a proof-focused geometry lesson plan for: "{query}"
-Include: student-friendly explanation, real-world link, and activity.
-"""
-    )
-}
+)
 
 # Optional: shortcut to solve simple math problems (like area of rectangle)
 def try_math_solver(query):
@@ -79,34 +73,31 @@ def try_math_solver(query):
         return f"The area of the rectangle is {l} × {w} = {l * w} square units."
     return None
 
-# RAG function
-def rag_query(query, mode="general"):
+# RAG function using unified intent-aware prompt
+def rag_query(query):
     docs = vector_store.similarity_search(query, k=2)
     context = "\n\n".join([doc.page_content for doc in docs])
-    prompt = templates[mode].format_prompt(context=context, query=query).to_string()
+    prompt = template.format_prompt(context=context, query=query).to_string()
     return llm.invoke(prompt).content
 
 # Gradio app function
-def ask_geometry_sol(query, mode):
+def ask_geometry_sol(query):
     math_result = try_math_solver(query)
     if math_result:
         return math_result
     try:
-        return rag_query(query, mode)
+        return rag_query(query)
     except Exception as e:
         return f"⚠️ Error: {type(e).__name__} - {str(e)}"
 
-# Gradio UI
+# Gradio UI (no need for manual response type selection anymore!)
 iface = gr.Interface(
     fn=ask_geometry_sol,
-    inputs=[
-        gr.Textbox(label="Enter your Geometry SOL question or topic"),
-        gr.Radio(["general", "lesson plan", "worksheet", "proofs"], value="general", label="Response type")
-    ],
+    inputs=gr.Textbox(label="Enter your Geometry SOL question or topic"),
     outputs="text",
     title="📘 Virginia Geometry SOL Assistant",
-    description="Ask about any 2023 Geometry SOL (Standards of Learning). Get exact quotes, lesson plans, worksheets, or proof-based lessons."
+    description="Ask about any 2023 Geometry SOL (Standards of Learning). The assistant will auto-detect if you want a lesson plan, worksheet, proof, flashcards, or SOL reference."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()