test

Author: heyronjmilton

.gitignore

@@ -1,3 +1,4 @@
 venv/
 Backend/requirements.txt
-dat.txt
+dat.txt
+temp.txt

Backend/__pycache__/summariser.cpython-310.pyc

@@ -1,29 +1,56 @@
 import openai
 import shutil
 import os
+import chardet
 from fastapi import FastAPI,UploadFile,File
 from fastapi import APIRouter
 
-def temp_file(file):
-    try:
-        with open("dat.txt", "wb") as buffer:      # saving file
-            shutil.copyfileobj(file.file, buffer)
-    finally:
-        file.file.close()
 
-    with open("temp.txt", "r", encoding='utf-8') as file:
-        text = file.read()
+def detect_encoding(data):
+    result = chardet.detect(data)
+    encoding = result["encoding"]
+    return encoding
 
-    print("Deleting the saved file.......")
-    os.remove("temp.txt")
-    print("deleted....")       
-    return text
+router_generate_question = APIRouter()
 
 
-router_generate_question = APIRouter()
+@router_generate_question.post("/gen-questions")
+async def gen_questions(sorted_q: UploadFile = File(...),notebook_q: UploadFile = File(...),imp_topics: UploadFile = File(...),ref_paper: UploadFile = File(...)):
+    #
+    sorted_questions = await sorted_q.read()
+    notebook_questions = await notebook_q.read()
+    important_topics = await imp_topics.read()
+    reference_paper = await ref_paper.read()
+
+    # Decode the file content using the detected encoding
+    decoded_sorted_questions = sorted_questions.decode(detect_encoding(sorted_questions))
+    decoded_notebook_questions = notebook_questions.decode(detect_encoding(notebook_questions))
+    decoded_important_topics = important_topics.decode(detect_encoding(important_topics))
+    decoded_reference_paper = reference_paper.decode(detect_encoding(reference_paper))
+
+
+    prompt = f"generate 10 questions that are not in reference paper but similar \nSorted Previous Year Questions:\n{decoded_sorted_questions}\n\nNotebook Question List:\n{decoded_notebook_questions}\n\nImportant Topics:\n{decoded_important_topics}\n\nReference Paper:\n{decoded_reference_paper}\n\n"
+
+
+# Generate questions using OpenAI API
+    response = openai.Completion.create(
+        engine="davinci",
+        prompt=prompt,
+        max_tokens=1000,  # Adjust the max_tokens value as per your requirement
+        temperature=0.4,  # Adjust the temperature value to control the creativity of the generated questions
+        top_p=1.0,
+        frequency_penalty=0.0,
+        presence_penalty=0.0,
+        n=5  # Adjust the 'n' value to generate more or fewer questions
+    )
+
+    # Extract the generated questions from the API response
+    generated_questions = [choice['text'].strip() for choice in response.choices]
+
+    # Print the generated questions
+    for i, question in enumerate(generated_questions):
+        print(f"Question {i+1}: {question}")   
 
 
 
-def gen_questions(file: UploadFile = File(...)):
-    text = temp_file(file)  
-    return text
+    return {"dat1" :decoded_sorted_questions,"dat2" : decoded_notebook_questions,"dat3" : decoded_important_topics,"dat4" : decoded_reference_paper}

Backend/__pycache__/test.cpython-310.pyc

@@ -2,15 +2,17 @@
 from fastapi import FastAPI
 #from Backend.pyqsorter import router as api1_router
 from Backend.summariser import router_summariser as summariser
+from Backend.test import router_generate_question as generate_question
 #from Backend.Notes_Analyser import router as api4_router
 # import other API routers as needed
 
 app = FastAPI()
 
 
-# Mount the API routerss
+# Mount the API routers
 #app.include_router(api1_router)
-app.include_router(summariser)
+#app.include_router(summariser)
+app.include_router(generate_question)
 #app.include_router(api4_router)
 
 # include other API routers as needed