link

Author: ANGELOANTU7

Backend/LatestSorter.py

@@ -42,8 +42,8 @@ def get_text_from_s3(bucket_name, file_name, encoding='utf-8'):
 app = APIRouter()
 
 @app.get("/sorter")
-def process_files():
-    
+def process_files(user:str):
+    user=user+"/"
         # Make an API request with a reset message
     response = openai.ChatCompletion.create(
         model="gpt-3.5-turbo",
@@ -62,13 +62,13 @@ def process_file(file_name):
 
         # Read file from S3
 
-        response = s3.get_object(Bucket='learnmateai', Key='pyqs_txt/' + file_name)
+        response = s3.get_object(Bucket='learnmateai', Key=user+'pyqs_txt/' + file_name)
         file_content = response['Body'].read().decode('utf-16-le')
 
         # Split file content into batches (adjust batch size as needed)
         batch_size = 30000
         batches = [file_content[i:i+batch_size] for i in range(0, len(file_content), batch_size)]
-        response2 = s3.get_object(Bucket='learnmateai', Key= "syllabus_pdf/syllabus.txt")
+        response2 = s3.get_object(Bucket='learnmateai', Key= user+"syllabus_pdf/syllabus.txt")
         topics = response2['Body'].read().decode('utf-8')
         # Process batches
         Sorted_PYQ_Mod=[[]for _ in range(5)]
@@ -158,4 +158,4 @@ def process_file(file_name):
         file_name = file['Key'].split('/')[-1]
         process_file(file_name)
 
-    return {"message": "File processing completed."}
+    return {"message": "PYQS SORTED"}

Backend/NotesChunker.py

@@ -25,11 +25,12 @@ def upload_to_s3(bucket_name, folder_name, file_name, content):
 app = APIRouter()
 
 @app.get("/process_files")
-def process_files():
+def process_files(user: str):
+    user=user+"/"
     # Function to read and process a file
     def process_file(file_name):
         # Read file from S3
-        response = s3.get_object(Bucket='learnmateai', Key='notes_txt/' + file_name)
+        response = s3.get_object(Bucket='learnmateai', Key=user+'notes_txt/' + file_name)
         file_content = response['Body'].read().decode('utf-8')
 
         # Split file content into batches (adjust batch size as needed)
@@ -61,7 +62,7 @@ def process_file(file_name):
 
             bucket_name = 'learnmateai'
             file=file_name.split(".")[0]
-            folder_name = f'Analysed_Notes/{file}'
+            folder_name = f'{user}Analysed_Notes/{file}'
 
             for i, batch in enumerate(text_batches):
                 lines = batch.split('\n')
@@ -73,11 +74,11 @@ def process_file(file_name):
                 print(f"File '{file_name1}' uploaded to '{bucket_name}/{folder_name}'")
 
     # Get the list of files in the "notes_txt" folder
-    response = s3.list_objects_v2(Bucket='learnmateai', Prefix='notes_txt/')
+    response = s3.list_objects_v2(Bucket='learnmateai', Prefix=user+'notes_txt/')
 
     # Process each file
     for file in response['Contents']:
         file_name = file['Key'].split('/')[-1]
         process_file(file_name)
 
-    return {"message": "File processing completed."}
+    return {"message": "NOTES"}

Backend/Processor.py

@@ -1,23 +1,42 @@
-from fastapi import APIRouter
+from fastapi import APIRouter,Form
 import httpx
 
 app = APIRouter()
 
-@app.get("/fetch-data")
-async def fetch_data():
+@app.get("/processor")
+async def fetch_data(link:str,receiver_email: str ):
     results = []
 
     async with httpx.AsyncClient() as client:
         # Make the first API call
-        response1 = await client.get("https://api.example.com/endpoint1")
+        response1 = await client.get(link+"/process_files")
         results.append(response1.json())
+        print(response1)
 
-        # Make the second API call
-        response2 = await client.get("https://api.example.com/endpoint2")
+        response2 = await client.get(link+"/sorter")
         results.append(response2.json())
+        print(response2)
 
-        # Make the third API call
-        response3 = await client.get("https://api.example.com/endpoint3")
+        response3 = await client.get(link+"/card-json")
         results.append(response3.json())
+        print(response3)
+        
+        response4 = await client.get(link+"note_gen")
+        results.append(response4.json())
+        print(response4)
+        
+        response5 = await client.get(link+"/question_gen")
+        results.append(response5.json())
+        print(response5)
+        
+        payload = {
+            "receiver_email": receiver_email,
+            "subject": "LearnMateAI",
+            "message": "Your files have been processed successfully"
+        }
+        
+        response6 = await client.post(link+"/email", data=payload)
+        results.append(response6.json())
+        print(response6)
 
     return results

Backend/Questionare_Creater.py

@@ -27,7 +27,7 @@ async def summarize_s3_files():
             summary = await summarize_file(bucket_name, file_key,file_name)
             print(summary)
             save_summary(file_name, summary)
-        return {'message': 'Created Notes and saved successfully.'}
+        return {'message': 'Created MCQs and saved successfully.'}
     except Exception as e:
         return {'error': str(e)}
 

Backend/__pycache__/LatestSorter.cpython-310.pyc

@@ -11,8 +11,9 @@
 
 
 @router.post("/email")
-def send_email_route(sender_email: str = "mailgun@sandbox1b21493cde82477e8fe09dbc04c09eb4.mailgun.org", receiver_email: str = Form(...), subject: str = Form(...), message: str = Form(...)):
+def send_email_route(receiver_email: str = Form(...), subject: str = Form(...), message: str = Form(...)):
     url = f"https://api.mailgun.net/v3/{MAILGUN_DOMAIN}/messages"
+    sender_email="angeloantu@gmail.com"
     auth = ("api", MAILGUN_API_KEY)
     data = {
         "from": sender_email,