shifted server from node->starlette

Softlab.yml

@@ -26,13 +26,18 @@ dependencies:
     - boto==2.49.0
     - boto3==1.10.4
     - botocore==1.13.4
+    - cachetools==3.1.1
     - chardet==3.0.4
+    - click==7.0
     - docutils==0.15.2
     - gast==0.2.2
     - gensim==3.8.1
+    - google-auth==1.6.3
     - google-pasta==0.1.7
     - grpcio==1.24.3
+    - h11==0.8.1
     - h5py==2.10.0
+    - httptools==0.0.13
     - idna==2.8
     - jmespath==0.9.4
     - keras-applications==1.0.8
@@ -42,17 +47,24 @@ dependencies:
     - numpy==1.17.3
     - opt-einsum==3.1.0
     - protobuf==3.10.0
+    - pyasn1==0.4.7
+    - pyasn1-modules==0.2.7
     - python-dateutil==2.8.0
     - requests==2.22.0
+    - rsa==4.0
     - s3transfer==0.2.1
     - scipy==1.3.1
     - six==1.12.0
     - smart-open==1.8.4
+    - starlette==0.12.10
     - tensorboard==2.0.0
     - tensorflow==2.0.0
     - tensorflow-estimator==2.0.1
     - termcolor==1.1.0
     - urllib3==1.25.6
+    - uvicorn==0.9.1
+    - uvloop==0.13.0
+    - websockets==8.0.2
     - werkzeug==0.16.0
     - wrapt==1.11.2
 prefix: /home/shreyansh/.conda/envs/Softlab

main.py

+from google.oauth2 import service_account
+from google.auth.transport.requests import AuthorizedSession
+from starlette.applications import Starlette
+from starlette.responses import JSONResponse, PlainTextResponse
+from prediction import predict
+import uvicorn
+import json
+
+scopes = [
+  "https://www.googleapis.com/auth/userinfo.email",
+  "https://www.googleapis.com/auth/firebase.database"
+]
+
+app = Starlette(debug=True)
+credentials = service_account.Credentials.from_service_account_file("./serviceKey.json", scopes=scopes)
+authed_session = AuthorizedSession(credentials)
+
+
+@app.route('/')
+async def homepage(request):
+    return JSONResponse({'hello': 'world'})
+
+
+@app.route('/{prompt}')
+async def getEssay(request):
+
+    prompt = request.path_params['prompt']
+    response = authed_session.get("https://softlab-ba722.firebaseio.com/"+prompt+".json")
+    return JSONResponse(response.json())
+
+
+# app.add_route('/evaluate', predict, methods=["POST"])
+@app.route('/evaluate',methods=["POST"])
+async def evaluate(request):
+    body = await request.json()
+    score = predict(body['essay'])
+    return PlainTextResponse(score)
+
+
+@app.route('/contribute',methods=["POST"])
+async def evaluate(request):
+    body = await request.json()
+    score, essay, prompt = body['score'], body['essay'], body['prompt']
+    json = { 
+        "essay": essay,
+        "score": score
+    }
+    message = "Success, Thanks for your envaluable contribution!"
+    response = authed_session.post("https://softlab-ba722.firebaseio.com/"+prompt+"/.json",None,json)
+    print(response)
+    if response.status_code!=200:
+        message = "Oops some error occured please try after some time."
+    return PlainTextResponse(message)
+
+if __name__ == '__main__':
+    uvicorn.run(app, host='0.0.0.0', port=8000)
\ No newline at end of file

prediction.py

@@ -40,18 +40,17 @@ def getAvgFeatureVecs(essays, model, num_features):
         counter = counter + 1
     return essayFeatureVecs
 
+def predict(essay):
+    model = KeyedVectors.load_word2vec_format('models/word2vecmodel.bin', binary=True)
+    essay_tokens = []
 
-model = KeyedVectors.load_word2vec_format('models/word2vecmodel.bin', binary=True)
-essay_tokens = []
-essay = sys.argv[1]
+    essay_tokens.append(tokenizeEssay(essay))
+    testEssayVec = getAvgFeatureVecs(essay_tokens, model, 300)
 
-essay_tokens.append(tokenizeEssay(essay))
-testEssayVec = getAvgFeatureVecs(essay_tokens, model, 300)
+    testEssayVec = np.array(testEssayVec)
+    testEssayVec = np.reshape(testEssayVec,(testEssayVec.shape[0], 1, testEssayVec.shape[1]))
 
-testEssayVec = np.array(testEssayVec)
-testEssayVec = np.reshape(testEssayVec,(testEssayVec.shape[0], 1, testEssayVec.shape[1]))
-
-evaluator = keras.models.load_model('models/final_lstm.h5')
-y_pred = evaluator.predict(testEssayVec)
-y_pred = np.around(y_pred)
-print(y_pred.reshape(-1)[0])
\ No newline at end of file
+    evaluator = keras.models.load_model('models/final_lstm.h5')
+    y_pred = evaluator.predict(testEssayVec)
+    y_pred = 0 if np.isnan(y_pred[0][0]) else np.around(y_pred[0][0])
+    return str(y_pred)
\ No newline at end of file