API

app.py

@@ -2,137 +2,52 @@
 from fastapi import FastAPI,File, HTTPException
 from pydantic import BaseModel
 import json
-
-
 from typing import List, Dict, Any
 import pandas as pd
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
 from scipy import sparse
 from datetime import datetime
-
-#SkillExtraction Packages
-import psycopg2
-import pandas as pd
-import numpy as np
-from sentence_transformers import SentenceTransformer
-import spacy
-from sklearn.metrics.pairwise import cosine_similarity
-from spacy.matcher import PhraseMatcher
-from skillNer.general_params import SKILL_DB
-from skillNer.skill_extractor_class import SkillExtractor
-from psycopg2.extensions import register_adapter, AsIs
-register_adapter(np.int64, AsIs)
 import warnings
-warnings.filterwarnings('ignore')
+import os
+import logging
 
+warnings.filterwarnings('ignore')
 
-#Custom Classes for endpoints
-from DbConnection import DbConnection
-from UploadFile import UploadOpenFile
-from SkillExtract import SkillExtractorDetails
-from ExtractContentsFromFile import ExtractContentFromFile
-from RemoveSkills import RemoveSkill
-from AddSkillDetails import AddSkill
-from SkillMatcher import SkillMatch
-from SkillExtractV1 import SkillExtractorDetailsV1
-import ClassModals
-import os
-os.environ['HF_HOME'] = '/hug/cache/'
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 app = FastAPI()
-         
-nlp = spacy.load("en_core_web_lg")
-    # init skill extractor
-skill_extractor = SkillExtractor(nlp, SKILL_DB, PhraseMatcher)
-model = SentenceTransformer('all-MiniLM-L6-v2')
 
 # Get the current directory path
 current_dir = os.path.dirname(os.path.abspath(__file__))
 excel_path = os.path.join(current_dir, 'datasetsample.xlsx')
 
-purchase_history = pd.read_excel(excel_path, sheet_name='Transaction History', 
-                               parse_dates=['Purchase_Date'])
-purchase_history['Customer_Id'] = purchase_history['Customer_Id'].astype(str)
-product_categories = purchase_history[['Product_Id', 'Category']].drop_duplicates().set_index('Product_Id')['Category'].to_dict()
-purchase_counts = purchase_history.groupby(['Customer_Id', 'Product_Id']).size().unstack(fill_value=0)
-sparse_purchase_counts = sparse.csr_matrix(purchase_counts)
-cosine_similarities = cosine_similarity(sparse_purchase_counts.T)
-
-
-@app.get("/")
-async def root():
- return {"Recommendation":"Recommendation Version 1.00, https://vaibhav84-recommendation.hf.space/redoc , https://vaibhav84-recommendation.hf.space/docs"}
- 
-def parse_csv(df):
-    res = df.to_json(orient="records")
-    parsed = json.loads(res)
-    return parsed
-
-@app.post("/CustomerLogin/")
-def UploadJobDescription(CustomerID : str, CustomerPwd: str):
-    try:   
-        if CustomerID != "" and CustomerPwd == (CustomerID + "123"):
-            return "Login Successful"   
-        else:
-            return "Login Failed"        
-    except Exception as e:
-        return "An error occurred: {e}" 
-
-@app.get("/recommendations/{customer_id}")
-async def get_recommendations(customer_id: str, n: int = 5):
-    """
-    Get recommendations for a customer
+# Log the file path and directory contents for debugging
+logger.info(f"Current directory: {current_dir}")
+logger.info(f"Excel path: {excel_path}")
+logger.info("Directory contents:")
+for file in os.listdir(current_dir):
+    logger.info(f"- {file}")
+
+try:
+    # Load the data when the application starts
+    purchase_history = pd.read_excel(excel_path, sheet_name='Transaction History', 
+                                   parse_dates=['Purchase_Date'])
+    logger.info("Successfully loaded Excel file")
     
-    Parameters:
-    - customer_id: The ID of the customer
-    - n: Number of recommendations to return (default: 5)
+    purchase_history['Customer_Id'] = purchase_history['Customer_Id'].astype(str)
+    product_categories = purchase_history[['Product_Id', 'Category']].drop_duplicates().set_index('Product_Id')['Category'].to_dict()
+    purchase_counts = purchase_history.groupby(['Customer_Id', 'Product_Id']).size().unstack(fill_value=0)
+    sparse_purchase_counts = sparse.csr_matrix(purchase_counts)
+    cosine_similarities = cosine_similarity(sparse_purchase_counts.T)
     
-    Returns:
-    - JSON object containing purchase history and recommendations
-    """
-    try:
-        purchased_items, recommended_items = get_customer_items_and_recommendations(customer_id, n)
-        
-        return {
-            "customer_id": customer_id,
-            "purchase_history": purchased_items,
-            "recommendations": recommended_items
-        }
-    except Exception as e:
-        raise HTTPException(status_code=404, detail=f"Error processing customer ID: {customer_id}. {str(e)}")
-
-
-
-@app.post("/UploadJobDescription/")
-async def UploadJobDescription(file: bytes =  File(...), FileName: str = "sample.pdf"):
-    try:   
-        text= ExtractContentFromFile.ExtractDataFromFile(FileName,file)
-        returnSkills = SkillExtractorDetailsV1.GetSkillData(skill_extractor,text)     
-        return parse_csv(returnSkills) 
-    except Exception as e:
-        return "An error occurred: {e}" 
-
-
-@app.delete("/RemoveSkillsByName/")
-def RemoveSkills(SkillName : str):    
-    RemoveSkill.RemoveSkillDetails(SkillName)
-    return "Skill Removed Successfully"
-
-@app.post("/AddSkillDetails/")
-def AddSkills(Skills : ClassModals.Modals.AddSkillDetails):    
-    skilldetailsStr = Skills.SkillName + ',' + Skills.SkillType + ',' + str(Skills.SkillScore)    
-    return AddSkill.AddSkillDetails(skilldetailsStr)
-
-@app.put("/UpdateSkillDetails/")
-def UpdateSkills(Skills : ClassModals.Modals.UpdateSkillDetails):    
-    skilldetailsStr = Skills.SkillName + ',' + str(Skills.SkillWeightage)
-    return AddSkill.UpdateSkillDetails(skilldetailsStr)
-
-@app.get("/GetAllSkillDetails/")
-def AllSkills():
-    return (AddSkill.GetSkillDetails())
-
+    logger.info("Data processing completed successfully")
+    
+except Exception as e:
+    logger.error(f"Error loading or processing data: {str(e)}")
+    raise
 
 def get_customer_items_and_recommendations(user_id: str, n: int = 5) -> tuple[List[Dict], List[Dict]]:
     """
@@ -178,6 +93,31 @@ def get_customer_items_and_recommendations(user_id: str, n: int = 5) -> tuple[Li
     ]
 
     return purchased_items_info, recommended_items_info
-#return JSONResponse(content={"message": "Here's your interdimensional portal." , "mes1":"data2"})
-#https://vaibhav84-resumeapi.hf.space/docs
-#https://vaibhav84-resumeapi.hf.space/redoc d
+
+@app.get("/")
+async def root():
+    return {"message": "Welcome to the Recommendation API"}
+
+@app.get("/recommendations/{customer_id}")
+async def get_recommendations(customer_id: str, n: int = 5):
+    """
+    Get recommendations for a customer
+    
+    Parameters:
+    - customer_id: The ID of the customer
+    - n: Number of recommendations to return (default: 5)
+    
+    Returns:
+    - JSON object containing purchase history and recommendations
+    """
+    try:
+        purchased_items, recommended_items = get_customer_items_and_recommendations(customer_id, n)
+        
+        return {
+            "customer_id": customer_id,
+            "purchase_history": purchased_items,
+            "recommendations": recommended_items
+        }
+    except Exception as e:
+        logger.error(f"Error processing request for customer {customer_id}: {str(e)}")
+        raise HTTPException(status_code=404, detail=f"Error processing customer ID: {customer_id}. {str(e)}")