Create app.py

app.py

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+import os
+import json
+from typing import Any, Dict, Optional
+
+from flask import Flask, request, jsonify
+
+from openai import OpenAI
+
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+
+# ----------------------------
+# Configuration
+# ----------------------------
+CHROMA_DIR = os.getenv("CHROMA_DIR", "./chroma_db")
+EMBEDDING_MODEL_NAME = os.getenv("EMBEDDING_MODEL_NAME", "thenlper/gte-large")
+OPENAI_MODEL = os.getenv("OPENAI_MODEL", "gpt-4.1-nano")
+
+# Hugging Face Spaces: put your key in "Settings -> Secrets" as OPENAI_API_KEY
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+if not OPENAI_API_KEY:
+    raise RuntimeError(
+        "Missing OPENAI_API_KEY environment variable. "
+        "In Hugging Face Spaces, add it in Settings -> Secrets."
+    )
+
+client = OpenAI(api_key=OPENAI_API_KEY)
+
+# ----------------------------
+# Prompt + JSON schema (from notebook)
+# ----------------------------
+DEV_PROMPT = """
+You are a Texas Grade 5 Mathematics tutor for kids, and you also support parents and teachers.
+Your tone must be kid-safe, friendly, clear, and encouraging. Keep explanations simple, accurate, and non-judgmental.
+
+CRITICAL OUTPUT RULES:
+- Output MUST be valid JSON only (no markdown, no code fences, no extra text).
+- Use double quotes for all JSON keys/strings.
+- Do not include trailing commas.
+- Keep responses safe for kids (no unsafe, hateful, sexual, violent, or scary content).
+
+SCOPE RULE:
+- Only answer mathematics (Grade 5 level preferred; you may briefly define advanced terms if asked).
+- If the user asks anything not related to mathematics, respond ONLY with:
+  {"type":"Refusal","message":"Sorry, I can't answer questions other than mathematics."}
+
+OUTPUT TYPES:
+1) Concept explanation:
+{
+  "type": "Concept",
+  "message": "Short kid-friendly explanation..."
+}
+
+2) Practice questions (MCQ):
+- Create up to 5 questions maximum. If user requests more than 5, generate only 5.
+- Each question MUST have exactly 4 answer choices.
+- The correct answer MUST be one of the 4 choices.
+- Provide the correct answer explicitly using "CorrectOption" (A/B/C/D) and "CorrectAnswer" (exact matching text from Answers).
+- Keep math appropriate and compute accurately. Avoid trick questions.
+
+{
+  "type": "Questions",
+  "message": [
+    {
+      "Q1": "Question text",
+      "Answers": { "A": "Option 1", "B": "Option 2", "C": "Option 3", "D": "Option 4" },
+      "CorrectOption": "B",
+      "CorrectAnswer": "Option 2"
+    }
+  ]
+}
+
+ACCURACY / ANTI-HALLUCINATION RULES:
+- Do the math carefully. Ensure only one correct option unless the user explicitly asks for multiple correct answers.
+- If you detect ambiguity (missing numbers/units), ask ONE clarifying question using:
+  {"type":"Concept","message":"I need one detail to answer: ..."}
+  (Keep it math-only and kid-safe.)
+
+STYLE GUIDELINES:
+- Use simple words and short sentences for kids.
+- For parents/teachers, add a brief note on how to support learning (1–2 sentences).
+- Avoid unrelated topics, brand names, or personal data requests.
+""".strip()
+
+JSON_SCHEMA: Dict[str, Any] = {
+    "type": "object",
+    "additionalProperties": False,
+    "required": ["type", "message"],
+    "properties": {
+        "type": {"type": "string", "enum": ["Concept", "Questions", "Refusal"]},
+        "message": {
+            "anyOf": [
+                {"type": "string"},
+                {
+                    "type": "array",
+                    "maxItems": 5,
+                    "items": {
+                        "type": "object",
+                        "additionalProperties": False,
+                        "required": ["Q1", "Answers", "CorrectOption", "CorrectAnswer"],
+                        "properties": {
+                            "Q1": {"type": "string"},
+                            "Answers": {
+                                "type": "object",
+                                "additionalProperties": False,
+                                "required": ["A", "B", "C", "D"],
+                                "properties": {
+                                    "A": {"type": "string"},
+                                    "B": {"type": "string"},
+                                    "C": {"type": "string"},
+                                    "D": {"type": "string"},
+                                },
+                            },
+                            "CorrectOption": {"type": "string", "enum": ["A", "B", "C", "D"]},
+                            "CorrectAnswer": {"type": "string"},
+                        },
+                    },
+                },
+            ]
+        },
+    },
+}
+
+# ----------------------------
+# Vector DB + Retriever (loaded once at startup)
+# ----------------------------
+embedding_model = SentenceTransformerEmbeddings(model_name=EMBEDDING_MODEL_NAME)
+
+vectorstore = Chroma(
+    persist_directory=CHROMA_DIR,
+    embedding_function=embedding_model,
+)
+
+retriever = vectorstore.as_retriever(
+    search_type="similarity",
+    search_kwargs={"k": 3},
+)
+
+# ----------------------------
+# RAG helpers
+# ----------------------------
+def generate_context_from_input(user_input: str) -> str:
+    """Retrieve relevant chunks from the vector store and return as a single context string."""
+    rel_docs = retriever.invoke(user_input)
+    context_list = [d.page_content for d in rel_docs]
+    return ". ".join(context_list)
+
+def get_llm_response(user_input: str, context: str = "") -> str:
+    """Call OpenAI Chat/Responses API and return the model output text (JSON string)."""
+    messages = [{"role": "developer", "content": DEV_PROMPT}]
+
+    if context and context.strip():
+        messages.append(
+            {
+                "role": "developer",
+                "content": (
+                    "Use the following CONTEXT only if it is relevant to the user's request. "
+                    "Do not invent facts that are not in the context.\n"
+                    "BEGIN_CONTEXT\n"
+                    f"{context}\n"
+                    "END_CONTEXT"
+                ),
+            }
+        )
+
+    messages.append({"role": "user", "content": user_input})
+
+    resp = client.responses.create(
+        model=OPENAI_MODEL,
+        input=messages,
+        temperature=0.2,
+        max_output_tokens=800,
+        text={
+            "format": {
+                "type": "json_schema",
+                "name": "grade5_math_response",
+                "strict": True,
+                "schema": JSON_SCHEMA,
+            }
+        },
+    )
+    return resp.output_text
+
+def generate_response_from_rag(user_input: str) -> Dict[str, Any]:
+    context = generate_context_from_input(user_input)
+    raw = get_llm_response(user_input=user_input, context=context)
+
+    # Ensure we return valid JSON to the client even if model output is slightly off.
+    try:
+        return json.loads(raw)
+    except Exception:
+        return {"type": "Concept", "message": raw}
+
+# ----------------------------
+# Flask API
+# ----------------------------
+app = Flask(__name__)
+
+@app.get("/")
+def health():
+    return jsonify({"status": "ok"})
+
+@app.post("/MathQuestion")
+def math_question():
+    payload = request.get_json(silent=True) or {}
+    query = payload.get("Query") or payload.get("query")
+
+    if not query or not isinstance(query, str):
+        return jsonify({"error": 'Missing required field "Query" (string).'}), 400
+
+    try:
+        result = generate_response_from_rag(query.strip())
+        return jsonify(result)
+    except Exception as e:
+        # Avoid leaking secrets; return safe error.
+        return jsonify({"error": "Server error while generating response.", "details": str(e)}), 500
+
+
+if __name__ == "__main__":
+    # Hugging Face Spaces (Docker) expects port 7860
+    port = int(os.getenv("PORT", "7860"))
+    app.run(host="0.0.0.0", port=port)