added some stuff

app.py

@@ -1,7 +1,7 @@
 from io import BytesIO
 from dotenv import load_dotenv
 import os
-from utils import google_search,split_text_into_chunks,insert_embeddings_into_pinecone_database,query_vector_database,generate_embedding_for_user_resume,delete_vector_namespace
+from utils import google_search,split_text_into_chunks,insert_embeddings_into_pinecone_database,query_vector_database,generate_embedding_for_user_resume,delete_vector_namespace,create_user,login_user
 from fastapi import FastAPI, File, UploadFile
 from fastapi.responses import JSONResponse
 import docx
@@ -16,15 +16,34 @@ CX = os.getenv("SEARCH_ENGINE_ID")
 API_KEY = os.getenv("GOOGLE_API_KEY")
 PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
 GEMINI_API_KEY=os.getenv("GEMINI_API_KEY")
+MONGO_URI=os.getenv("MONGO_URI")
 app = FastAPI()
 
 import re
 
+class UserBody(BaseModel):
+    Email:str
+    Password:str
+    
+class AiAnalysis(BaseModel):
+    UserId:str
+    Query:str
+
+class UserCourse(BaseModel):
+    EmploymentStatus:str
+    InterimRole:str
+    DesiredRole:str
+    Motivation:str
+    LearningPreference:str
+    HoursSpentLearning:str
+    Challenges:str
+    TimeframeToAchieveDreamRole:str
+    userId:str
 
 
 class CourseRecommendation(BaseModel):
-    coursename: str
-    completiontime: str
+    CourseName: str
+    CompletionTime: str
 
 def extract_course_info(text: str) -> CourseRecommendation:
     # Example regex patterns – adjust these as needed based on the response format.
@@ -37,7 +56,7 @@ def extract_course_info(text: str) -> CourseRecommendation:
     coursename = course_match.group(1).strip() if course_match else "Unknown"
     completiontime = time_match.group(0).strip() if time_match else "Unknown"
 
-    return CourseRecommendation(coursename=coursename, completiontime=completiontime)
+    return CourseRecommendation(CourseName=coursename, CompletionTime=completiontime)
 
 
 
@@ -78,12 +97,10 @@ def get_course_func(query):
             link = item.get('link')
             snippet = item.get('snippet')
             content_structure={}
-            
             content_structure["Course_Title"]=title
             content_structure["Course_Link"]=link
             content_structure["Course_Snippet"]= snippet
             content_structure["Scraped_Course_Details"]= scrapeCourse(url=link)
-            
             content.append(content_structure)
         
     
@@ -100,7 +117,6 @@ async def upload_file(user_id,file: UploadFile = File(...)):
     content = await file.read()  # Read the file content (this will return bytes)
     sentences=[]
 
-    # Print file details for debugging
     print(f"File name: {file.filename}")
     print(f"File content type: {file.content_type}")
     print(f"File size: {file.size} bytes")
@@ -108,7 +124,6 @@ async def upload_file(user_id,file: UploadFile = File(...)):
     
     if "pdf" == file.filename.split('.')[1]:
         pdf_document = fitz.open(stream=BytesIO(content), filetype="pdf")
-        # Print the content of the file (if it's text, you can decode it)
         extracted_text = ""
         for page_num in range(pdf_document.page_count):
             page = pdf_document.load_page(page_num)
@@ -129,10 +144,10 @@ async def upload_file(user_id,file: UploadFile = File(...)):
 
 
 
-@app.get("/ask")
-def ask_ai_about_resume(query, user_id):
+@app.post("/ask")
+def ask_ai_about_resume(req:AiAnalysis):
     # Retrieve context from your vector database
-    context = query_vector_database(query=query, api_key=PINECONE_API_KEY, name_space=user_id)
+    context = query_vector_database(query=req.Query, api_key=PINECONE_API_KEY, name_space=req.UserId)
     
     # Ensure that an event loop is present in this thread.
     try:
@@ -148,15 +163,15 @@ def ask_ai_about_resume(query, user_id):
         model="gemini-2.0-flash", 
         contents=f"""
         Answer this question using the context provided:
-        question: {query}
+        question: {req.Query}
         context: {context}
         """
     )
     
     return {"Ai_Response":response.text}
 
-@app.get("/recommend/courses")
-def ask_ai_about_resume(employment_status:str,interim_role:str,desired_role:str,motivation:str,learning_preference:str,hours_spent_learning:str,challenges:str,timeframe_to_achieve_dream_role:str, user_id:str):
+@app.post("/recommend/courses")
+def ask_ai_about_resume(request:UserCourse):
     """
 User Profile Information for Career Development
 
@@ -214,18 +229,33 @@ Parameters:
         - "completiontime": an estimate of how long it would take to complete the course.
         Do not include any extra text.
         Recommend a course using this information below :
-        Which of the following best describes you?: {employment_status}
-        Would you like to prepare for an interim role to gain experience and income while pursuing your dream job?: {interim_role}
-        What is your desired role?: {desired_role}
-        Why do you want to achieve this desired role?: {motivation}
-        How do you prefer to learn new skills?: {learning_preference}
-        How many hours per day can you dedicate to learning?: {hours_spent_learning}
-        What are the biggest challenges or obstacles you face in reaching your dream role?: {challenges}
-        What is your ideal timeframe for achieving your dream role?: {timeframe_to_achieve_dream_role}
+        Which of the following best describes you?: {request.EmploymentStatus}
+        Would you like to prepare for an interim role to gain experience and income while pursuing your dream job?: {request.InterimRole}
+        What is your desired role?: {request.DesiredRole}
+        Why do you want to achieve this desired role?: {request.Motivation}
+        How do you prefer to learn new skills?: {request.LearningPreference}
+        How many hours per day can you dedicate to learning?: {request.HoursSpentLearning}
+        What are the biggest challenges or obstacles you face in reaching your dream role?: {request.Challenges}
+        What is your ideal timeframe for achieving your dream role?: {request.TimeframeToAchieveDreamRole}
         
     
         """
     )
     course_info = extract_course_info(response.text)
-    courses = get_course_func(query=course_info.coursename)
-    return courses
+    courses = get_course_func(query=course_info.CourseName)
+    return {"CourseInfo":course_info,"Courses":courses}
+
+
+
+@app.post("/login")
+def signUp(user:UserBody):
+    user ={"email":user.Email,"password":user.Password}
+    user_id= login_user(db_uri=MONGO_URI,db_name="crayonics",collection_name="users",document=user)
+    return {"user_id":user_id}
+
+
+@app.post("/signup")
+def signUp(user:UserBody):
+    user ={"email":user.Email,"password":user.Password}
+    user_id= create_user(db_uri=MONGO_URI,db_name="crayonics",collection_name="users",document=user)
+    return {"user_id":user_id}

utils.py

@@ -1,4 +1,5 @@
 import requests
+from pymongo import MongoClient
 
 def google_search(query, api_key, cx):
     url = f"https://www.googleapis.com/customsearch/v1?q={query}&key={api_key}&cx={cx}"
@@ -107,3 +108,74 @@ def split_text_into_chunks(text, chunk_size=400):
     chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
     return chunks
 
+
+
+
+
+def create_user(db_uri: str, db_name: str, collection_name: str, document: dict) -> str:
+    """
+    Inserts a new document into the specified MongoDB collection.
+
+    Parameters:
+        db_uri (str): MongoDB connection URI.
+        db_name (str): Name of the database.
+        collection_name (str): Name of the collection.
+        document (dict): The document to insert.
+
+    Returns:
+        str: The ID of the inserted document.
+    """
+    # Connect to MongoDB
+    client = MongoClient(db_uri)
+    db = client[db_name]
+    collection = db[collection_name]
+    
+    # Insert the document
+    s = collection.find_one(document)
+    if s==None:
+        result = collection.insert_one(document)
+        client.close()
+        return str(result.inserted_id)
+    else:
+        client.close()
+        return str(s['_id'])
+    
+    # Close the connection
+    
+    
+    
+
+
+def login_user(db_uri: str, db_name: str, collection_name: str, document: dict) -> str:
+    """
+    Inserts a new document into the specified MongoDB collection.
+
+    Parameters:
+        db_uri (str): MongoDB connection URI.
+        db_name (str): Name of the database.
+        collection_name (str): Name of the collection.
+        document (dict): The document to insert.
+
+    Returns:
+        str: The ID of the inserted document.
+    """
+    # Connect to MongoDB
+    client = MongoClient(db_uri)
+    db = client[db_name]
+    collection = db[collection_name]
+    
+    # Insert the document
+    s = collection.find_one(document)
+    if s==None:
+        return False
+    else:
+        if document['password']==s['password']:
+            client.close()
+            return str(s['_id'])
+        else:
+            return True
+    # Close the connection
+    
+    
+    
+