Create main.py

main.py

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+# fastapi_app.py
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from typing import List, Optional
+from langchain.document_loaders import WikipediaLoader
+from langchain_groq import ChatGroq
+
+app = FastAPI()
+
+# Replace with your actual Groq API key
+GROQ_API_KEY = "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"
+
+class QuizRequest(BaseModel):
+    search_query: str
+    complexity: str = "Hard"
+    quiz_type: str = "MCQs"
+
+class GradeRequest(BaseModel):
+    quiz: str
+    answers: str
+    context: Optional[str] = None
+
+def get_llm():
+    return ChatGroq(
+        model="meta-llama/llama-4-scout-17b-16e-instruct",
+        temperature=0,
+        max_tokens=1024,
+        api_key=GROQ_API_KEY
+    )
+
+def wikipedia_query(search_query):
+    try:
+        data = WikipediaLoader(query=search_query, load_max_docs=2).load()
+        return data
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Wikipedia query failed: {e}")
+
+@app.post("/generate_quiz/")
+async def generate_quiz(request: QuizRequest):
+    context = wikipedia_query(request.search_query)
+    llm = get_llm()
+
+    prompt = f"""
+    You are a quiz generator assistant. Create a quiz for the given context.
+
+    Instructions:
+    Do not write answers in the quiz.
+    Quiz should be based on the following context:
+
+    context:
+    {context}
+
+    quiz complexity:
+    {request.complexity}
+
+    quiz type:
+    {request.quiz_type}
+
+    Your response:
+    """
+
+    result = llm.invoke(prompt)
+    return {"quiz": result.content, "context": str(context)}
+
+@app.post("/grade_quiz/")
+async def grade_quiz(request: GradeRequest):
+    llm = get_llm()
+
+    prompt = f"""
+    Check the quiz answers and give marks and also provide the correct answers. Use the following context to check the quiz. Write only the marks.
+
+    quiz:
+    {request.quiz}
+
+    answers:
+    {request.answers}
+
+    context:
+    {request.context}
+    """
+
+    result = llm.invoke(prompt)
+    return {"grade": result.content}