Update main.py

main.py

@@ -25,22 +25,25 @@ embeddings = None
 vectorstore = None
 retriever = None
 quiz_chain = None
+grade_chain = None
 
-# Request schema
 class QuizRequest(BaseModel):
-    topic: str
+    question: str
+
+class GradeRequest(BaseModel):
+    question: str  # string of Q/A pairs
 
 @app.on_event("startup")
 def load_components():
-    global llm, embeddings, vectorstore, retriever, quiz_chain
+    global llm, embeddings, vectorstore, retriever, quiz_chain, grade_chain
     try:
-        api_key = os.getenv("API_KEY")
+        api_key = os.getenv("api_key")
         if not api_key:
             logger.error("API_KEY environment variable is not set or empty.")
             raise RuntimeError("API_KEY environment variable is not set or empty.")
         logger.info("API_KEY is set.")
 
-        # Initialize LLM & Embeddings
+        # 1) Init LLM & Embeddings
         llm = ChatGroq(
             model="meta-llama/llama-4-scout-17b-16e-instruct",
             temperature=0,
@@ -53,7 +56,7 @@ def load_components():
             encode_kwargs={"normalize_embeddings": True},
         )
 
-        # Load FAISS indexes
+        # 2) Load FAISS indexes
         for zip_name, dir_name in [("faiss_index.zip", "faiss_index"), ("faiss_index(1).zip", "faiss_index_extra")]:
             if not os.path.exists(dir_name):
                 with zipfile.ZipFile(zip_name, 'r') as z:
@@ -71,24 +74,24 @@ def load_components():
         vectorstore = vs1
         logger.info("Merged FAISS indexes into a single vectorstore.")
 
-        retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+        retriever = vectorstore.as_retriever(search_kwargs={"k": 10})
 
-        # Quiz generation chain (with answers)
+        # Quiz generation chain
         quiz_prompt = PromptTemplate(
             template="""
-Generate a quiz on the topic "{topic}" using **only** the information in the "Retrieved context".
-Include clear questions, multiple-choice options (A, B, C, D), and **provide the correct answers** for each question.
+Generate a quiz on the topic "{question}" using **only** the information in the "Retrieved context".
+Include clear questions and multiple-choice options (A, B, C, D). Also provide the answers of the questions with them.
 If context is insufficient, reply with "I don't know".
 
 Retrieved context:
 {context}
 
 Quiz topic:
-{topic}
+{question}
 
-Quiz with answers:
+Quiz:
 """,
-            input_variables=["context", "topic"],
+            input_variables=["context", "question"],
         )
         quiz_chain = RetrievalQA.from_chain_type(
             llm=llm,
@@ -99,21 +102,23 @@ Quiz with answers:
         )
         logger.info("Quiz chain ready.")
 
+       
     except Exception as e:
         logger.error("Error loading components", exc_info=True)
         raise
 
 @app.get("/")
 def root():
-    return {"message": "Quiz API is up and running!"}
+    return {"message": "API is up and running!"}
 
 @app.post("/quiz")
 def create_quiz(request: QuizRequest):
     try:
-        logger.info("Generating quiz for topic: %s", request.topic)
-        result = quiz_chain.invoke({"topic": request.topic})
+        logger.info("Generating quiz for topic: %s", request.question)
+        result = quiz_chain.invoke({"query": request.question})
         logger.info("Quiz generated successfully.")
         return {"quiz": result.get("result")}
     except Exception as e:
         logger.error("Error generating quiz", exc_info=True)
         raise HTTPException(status_code=500, detail=str(e))
+