cahnges

ExtractContentsFromFile.py

@@ -0,0 +1,20 @@
+import io 
+import PyPDF2
+import docx2txt 
+class ExtractContentFromFile:
+    def ExtractDataFromFile(FileName,file):
+        text =''
+        print(text)
+        if FileName.endswith("pdf"):
+            reserve_pdf_on_memory = io.BytesIO(file)
+            load_pdf = PyPDF2.PdfReader(reserve_pdf_on_memory)
+            for page in load_pdf.pages:
+                text += page.extract_text()
+        
+        elif FileName.endswith("doc") or FileName.endswith("docx"):
+            text = docx2txt.process(file)
+            text = text.read()
+        
+        else:
+            text = file.decode('utf-8')
+        return text    

app.py

@@ -35,6 +35,7 @@ import requests
 from DbConnection import DbConnection
 from UploadFile import UploadOpenFile
 from SkillExtract import SkillExtractorDetails
+from ExtractContentsFromFile import ExtractContentFromFile
 import os
 os.environ['HF_HOME'] = '/hug/cache/'
 
@@ -71,29 +72,15 @@ def parse_csv(df):
 
 
 @app.post("/uploadJobDescription/")
-def uploadJobDescription(file: bytes =  File(...), FileName: str = "sample.pdf"):
-    # Save file locally for processing
-    text =''
-    print(text)
-    if FileName.endswith("pdf"):
-        reserve_pdf_on_memory = io.BytesIO(file)
-        load_pdf = PyPDF2.PdfReader(reserve_pdf_on_memory)
-        for page in load_pdf.pages:
-            text += page.extract_text()
-       
-    elif FileName.endswith("doc") or FileName.endswith("docx"):
-        text = docx2txt.process(file)
-        text = text.read()
-    
-    else:
-        text = file.decode('utf-8')
-
+def uploadJobDescription(file: bytes =  File(...), FileName: str = "sample.pdf"):   
+    text= ExtractContentFromFile.ExtractDataFromFile(FileName,file)
     returnID = UploadOpenFile.uploadFile(text,FileName,db_params)
     returnSkills = SkillExtractorDetails.SkillExtract(db_params,skill_extractor,returnID)     
     details = returnSkills.split('@')
     data = {'Data':['Required Skills', 'Soft Skills', 'Good to have Skills'], 'Values':[details[0], details[1], details[2]]}
     df = pd.DataFrame(data)
     return parse_csv(df) 
+
 @app.get("/AllProfileMatchResults") 
 def AllProfileMatchResults():
    dbQuery = "select * from profilematch"
@@ -121,5 +108,16 @@ def ExtractSkillsByJobID(skill_data: SkillDetails):
    skill_data.goodToHaveSkills = details[1]
    return skill_data
 
-
-   #return JSONResponse(content={"message": "Here's your interdimensional portal." , "mes1":"data2"})
+@app.post("/RemoveSkillsByName/")
+def RemoveSkills(SkillName : str):
+    conn = psycopg2.connect(**db_params)
+    cursor = conn.cursor()  
+    SkillName = data.split(':')[1]
+    print("Removing Skills " +  SkillName)
+    query = "update skillmaster set weightage = 0 where skilldetails = (%s)"        
+    params = (SkillName,)  
+    cursor.execute(query, params)
+    conn.commit()
+    cursor.close()
+    conn.close() 
+#return JSONResponse(content={"message": "Here's your interdimensional portal." , "mes1":"data2"})