app.py updated

app.py

@@ -8,6 +8,7 @@ import docx
 import fitz
 import asyncio
 from google import genai 
+from pydantic import BaseModel
 load_dotenv()
 
 CX = os.getenv("SEARCH_ENGINE_ID")
@@ -16,6 +17,30 @@ PINECONE_API_KEY=os.getenv("PINECONE_API_KEY")
 GEMINI_API_KEY=os.getenv("GEMINI_API_KEY")
 app = FastAPI()
 
+import re
+
+
+
+class CourseRecommendation(BaseModel):
+    coursename: str
+    completiontime: str
+
+def extract_course_info(text: str) -> CourseRecommendation:
+    # Example regex patterns – adjust these as needed based on the response format.
+    course_pattern =r'"coursename":\s*"([^"]+)"'
+    time_pattern = r"(\d+\s*-\s*\d+\s*months)"
+
+    course_match = re.search(course_pattern, text)
+    time_match = re.search(time_pattern, text)
+
+    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)
+
+
+
+
 @app.get("/get/course")
 def get_course(query):
 # Example search query
@@ -41,6 +66,31 @@ def get_course(query):
 
 
 
+def get_course_func(query):
+# Example search query
+    results = google_search(query, API_KEY, CX)
+    content=[]
+   
+    if results:
+        for item in results.get('items', []):
+            title = item.get('title')
+            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.append(content_structure)
+        
+    
+    return content
+            
+
+
+
+
 
 
 @app.post("/upload")
@@ -101,4 +151,79 @@ def ask_ai_about_resume(query, user_id):
         """
     )
     
-    return {"Ai Response":response.text}
+    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):
+    """
+User Profile Information for Career Development
+
+This section defines the parameters used to gather information from the user to understand their current employment situation, learning preferences, challenges, and goals related to achieving their dream role.
+
+Parameters:
+
+    employment_status (str): 
+        A description of the user's current employment situation (e.g., "unemployed", "part-time", "full-time").
+    
+    interim_role (str): 
+        Indicates whether the user is willing to prepare for an interim role to gain experience and income while pursuing their dream role (e.g., "yes" or "no").
+    
+    desired_role (str): 
+        The role the user ultimately wishes to obtain (e.g., "Full-Stack Developer", "Data Scientist").
+    
+    motivation (str): 
+        The user's reasons or motivations for pursuing the desired role.
+    
+    learning_preference (str): 
+        Describes how the user prefers to learn new skills (e.g., "online courses", "self-study", "bootcamp").
+    
+    hours_spent_learning (str or int): 
+        The number of hours per day the user can dedicate to learning.
+    
+    challenges (str): 
+        Outlines any obstacles or challenges the user faces in reaching their dream role.
+    
+    timeframe_to_achieve_dream_role (str): 
+        The ideal timeframe the user has in mind for achieving their dream role (e.g., "6-12 months").
+    
+    user_id (str): 
+        A unique identifier for the user; used to query personalized data from a vector database or other services.
+
+    """
+    
+
+    # Retrieve context from your vector database
+    
+    # Ensure that an event loop is present in this thread.
+    try:
+        loop = asyncio.get_event_loop()
+    except RuntimeError:
+        loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(loop)
+    
+    # Create the Gemini client after the event loop is set up
+    client = genai.Client(api_key=GEMINI_API_KEY)
+    
+    response = client.models.generate_content(
+        model="gemini-2.0-flash", 
+        contents=f"""
+        please respond with a JSON object that contains the following keys as a response:
+        - "coursename": the name of the recommended course,
+        - "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}
+        
+    
+        """
+    )
+    course_info = extract_course_info(response.text)
+    courses = get_course_func(query=course_info.coursename)
+    return courses