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