d

main.py

@@ -10,25 +10,6 @@ from transformers import pipeline
 
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 print("\n\n definition 2")
-def fit_threads(text):
-    define.join()
-
-    ######## Handel Sumarization model
-
-    a = threading.Thread(target=summarization, args=(text[0],))
-    b = threading.Thread(target=summarization, args=(text[1],))
-    c = threading.Thread(target=summarization, args=(text[-1],))
-
-    # Start all threads
-    a.start()
-    b.start()
-    c.start()
-
-    # Wait for all threads to finish
-    a.join()
-    b.join()
-    c.join()
-    print("Summarization Done")
 
 
 
@@ -47,10 +28,7 @@ df_vect = vectorizer.transform(x)
 ######### using summarizer model
 summ_data = []
 
-def summarization(text):
-    global summ_data
-    part = summarizer(text, max_length=150, min_length=30, do_sample=False)
-    summ_data.append(part[0]["summary_text"].replace("\xa0", ""))
+
 
 print("start api code")
 app = FastAPI(project_name="cv")
@@ -61,6 +39,7 @@ async def read_root():
 
 @app.post("/prediction")
 async def detect(cv: UploadFile, number_of_jobs: int):
+    print("pf")
     
     if (type(number_of_jobs) != int) or (number_of_jobs < 1) or (number_of_jobs > df.shape[0]):
         raise HTTPException(
@@ -72,13 +51,16 @@ async def detect(cv: UploadFile, number_of_jobs: int):
             status_code=415, detail="Please inter PDF file "
         )
 
-
-
+    print("pf2")
+    
+    summ_data =[]
     cv_data = extract_text_from_pdf(await cv.read())
     index = len(cv_data)//3
     text = [cv_data[:index], cv_data[index:2*index], cv_data[2*index:]]
-    fit_threads(text)
-    
+    for i in text:
+        part = summarizer(i, max_length=150, min_length=30, do_sample=False)
+        summ_data.append(part[0]["summary_text"].replace("\xa0", ""))
+    print("pf3")
     data = " .".join(summ_data)
     summ_data.clear()
     cv_vect = vectorizer.transform([data])