Update app.py

app.py

@@ -1,87 +1,87 @@
-from fastapi import FastAPI, HTTPException
-from models import CVExtracted, InsertedText, JobAndCV, ClassificationResult, InsertedLink
-import os
-from io import BytesIO
-import extractor
-from datetime import datetime
-from PyPDF2 import PdfReader
-import requests
-import classificator
-
-os.environ['TRANSFORMERS_CACHE'] = '/transformers_cache'
-os.environ['HF_HOME'] = '/transformers_cache'
-
-
-
-app =  FastAPI()
-@app.get("/", response_model=dict[str, str])
-def getall():
-    return {"hello":"world"}
-
-
-@app.post("/ext", response_model=CVExtracted)
-async def extract(text: InsertedText):
-    dictresult = extractor.predict(text.text)
-    return CVExtracted(**dictresult)
-
-
-@app.post("/classify", response_model=ClassificationResult)
-async def classify(body:JobAndCV ):
-    mininmal_start = 0
-    maximal_end = 0
-    positions = []
-    userMajors = []
-    if len(body.cv.experiences) > 0:
-        mininmal_start = datetime.strptime(body.cv.experiences[0]['start'], "%Y-%m-%d").date() if body.cv.experiences[0].get('start') != None else datetime.today().date()
-        maximal_end = datetime.strptime(body.cv.experiences[0]['end'], "%Y-%m-%d").date()
-        for exp in body.cv.experiences:
-            positions.append(exp['position'])
-            if exp.get('end') == None:
-                exp['end'] = datetime.today().strftime("%Y-%m-%d")
-            if datetime.strptime(exp['start'], "%Y-%m-%d").date() < mininmal_start:
-                mininmal_start = datetime.strptime(exp['start'], "%Y-%m-%d").date()
-            if datetime.strptime(exp['end'], "%Y-%m-%d").date() > maximal_end:
-                maximal_end = datetime.strptime(exp['end'], "%Y-%m-%d").date()
-    else:
-        mininmal_start = 0
-        maximal_end = 0
-    
-    for edu in body.cv.educations:
-        userMajors.append(edu['major'])
-    
-    yoe = (maximal_end - mininmal_start).days//365  
-    cv = {
-        "experiences": str(body.cv.experiences), 
-        "positions": str(positions), 
-        "userMajors": str(userMajors), 
-        "skills": str(body.cv.skills), 
-        "yoe": yoe
-    }
-    job = {
-        "jobDesc": body.job.jobDesc, 
-        "role": body.job.role, 
-        "majors": str(body.job.majors), 
-        "skills": str(body.job.skills), 
-        "minYoE": body.job.minYoE
-    }
-    results = classificator.predict(cv, job)
-    return ClassificationResult(**results)
-
-@app.post("/cv", response_model=CVExtracted)
-async def extract(link: InsertedLink):
-    response = requests.get(link.link)
-    if response.status_code == 200:
-        # Open the PDF from bytes in memory
-        pdf_reader = PdfReader(BytesIO(response.content))
-        number_of_pages = len(pdf_reader.pages)
-        # Optionally, read text from the first page
-        page = pdf_reader.pages[0]
-        text = page.extract_text()
-        for i in range(1, number_of_pages):
-            text+= '\n' + pdf_reader.pages[i].extract_text()
-    else:
-        #return error, make 500 because file server error
-        raise HTTPException(status_code=response.status_code, detail="File server error")
-
-    dictresult = extractor.predict(text)
+from fastapi import FastAPI, HTTPException
+from models import CVExtracted, InsertedText, JobAndCV, ClassificationResult, InsertedLink
+import os
+from io import BytesIO
+import extractor
+from datetime import datetime
+from PyPDF2 import PdfReader
+import requests
+import classificator
+
+os.environ['TRANSFORMERS_CACHE'] = '/transformers_cache'
+os.environ['HF_HOME'] = '/transformers_cache'
+
+
+
+app =  FastAPI()
+@app.get("/", response_model=dict[str, str])
+def getall():
+    return {"hello":"world"}
+
+
+@app.post("/ext", response_model=CVExtracted)
+async def extract(text: InsertedText):
+    dictresult = extractor.predict(text.text)
+    return CVExtracted(**dictresult)
+
+
+@app.post("/classify", response_model=ClassificationResult)
+async def classify(body:JobAndCV ):
+    mininmal_start = 0
+    maximal_end = 0
+    positions = []
+    userMajors = []
+    if len(body.cv.experiences) > 0:
+        mininmal_start = datetime.strptime(body.cv.experiences[0]['start'], "%Y-%m-%d").date() if body.cv.experiences[0].get('start') != None else datetime.today().date()
+        maximal_end = datetime.strptime(body.cv.experiences[0]['end'], "%Y-%m-%d").date() if body.cv.experiences[0].get('end') != None else datetime.today().date()
+        for exp in body.cv.experiences:
+            positions.append(exp['position'])
+            if exp.get('end') == None:
+                exp['end'] = datetime.today().strftime("%Y-%m-%d")
+            if datetime.strptime(exp['start'], "%Y-%m-%d").date() < mininmal_start:
+                mininmal_start = datetime.strptime(exp['start'], "%Y-%m-%d").date()
+            if datetime.strptime(exp['end'], "%Y-%m-%d").date() > maximal_end:
+                maximal_end = datetime.strptime(exp['end'], "%Y-%m-%d").date()
+    else:
+        mininmal_start = 0
+        maximal_end = 0
+    
+    for edu in body.cv.educations:
+        userMajors.append(edu['major'])
+    
+    yoe = (maximal_end - mininmal_start).days//365  
+    cv = {
+        "experiences": str(body.cv.experiences), 
+        "positions": str(positions), 
+        "userMajors": str(userMajors), 
+        "skills": str(body.cv.skills), 
+        "yoe": yoe
+    }
+    job = {
+        "jobDesc": body.job.jobDesc, 
+        "role": body.job.role, 
+        "majors": str(body.job.majors), 
+        "skills": str(body.job.skills), 
+        "minYoE": body.job.minYoE
+    }
+    results = classificator.predict(cv, job)
+    return ClassificationResult(**results)
+
+@app.post("/cv", response_model=CVExtracted)
+async def extract(link: InsertedLink):
+    response = requests.get(link.link)
+    if response.status_code == 200:
+        # Open the PDF from bytes in memory
+        pdf_reader = PdfReader(BytesIO(response.content))
+        number_of_pages = len(pdf_reader.pages)
+        # Optionally, read text from the first page
+        page = pdf_reader.pages[0]
+        text = page.extract_text()
+        for i in range(1, number_of_pages):
+            text+= '\n' + pdf_reader.pages[i].extract_text()
+    else:
+        #return error, make 500 because file server error
+        raise HTTPException(status_code=response.status_code, detail="File server error")
+
+    dictresult = extractor.predict(text)
     return CVExtracted(**dictresult)