Modify API

main.py

@@ -14,16 +14,10 @@ app = FastAPI()
 
 
 class Data(BaseModel):
-    challenge: str
+    metric: str
-    dev_expected: str = ""
+    file_expected: str = ""
-    dev_out: str = ""
+    file_out: str = ""
-    testA_expected: str = ""
+    file_in: str = ""
-    testA_out: str = ""
-    testB_expected: str = ""
-    testB_out: str = ""
-    dev_in: str= ""
-    testA_in: str=""
-    testB_in: str=""
 
 
 def preprocess_data(out, expected):
@@ -61,15 +55,11 @@ def get_answerability_f1(trues, preds):
     return answerability_f1
 
 
-def get_scores(trues, preds):
+def get_final_score(trues, preds):
     scores = {}
     scores["Levenshtein"] = get_levenshtein_score(trues, preds)
     scores["AnswerabilityF1"] = get_answerability_f1(trues, preds)
-    scores["Final"] = round((scores["Levenshtein"] + scores["AnswerabilityF1"]) / 2, 2)
+    return round((scores["Levenshtein"] + scores["AnswerabilityF1"]) / 2, 2)
-
-    scores["Levenshtein"] = round(get_levenshtein_score(trues, preds), 2)
-    scores["AnswerabilityF1"] = round(get_answerability_f1(trues, preds), 2)
-    return scores
 
 
 def get_emotion_recognition_scores(df_in, df_expected, df_predition):
@@ -107,71 +97,79 @@ def get_emotion_recognition_scores(df_in, df_expected, df_predition):
 
 @app.get("/")
 async def root(data: Data):
-    challenge = data.challenge
+    metric = data.metric
-    dev_expected = data.dev_expected
+    file_expected = data.file_expected
-    dev_out = data.dev_out
+    file_out = data.file_out
-    testA_expected = data.testA_expected
+    file_in = data.file_in
-    testA_out = data.testA_out
-    testB_expected = data.testB_expected
-    testB_out = data.testB_out
-    dev_in = data.dev_in
-    testA_in = data.testA_in
-    testB_in = data.testB_in
 
-    if challenge == "QuestionAnswering":
+    try:
-        results = {}
+        if metric == "Final":
-        if len(dev_out) > 0:
+            if len(file_out) > 0:
-            dev_out, dev_expected = preprocess_data(dev_out, dev_expected)
+                file_out, file_expected = preprocess_data(file_out, file_expected)
-            if len(dev_out) != len(dev_expected):
+                if len(file_out) != len(file_expected):
                     return ERROR_RESPONSE
-            results["dev-0"] = get_scores(dev_expected, dev_out)
                 
-        if len(testA_out) > 0:
-            testA_out, testA_expected = preprocess_data(testA_out, testA_expected)
-            if len(testA_out) != len(testA_expected):
-                return ERROR_RESPONSE
-            results["test-A"] = get_scores(testA_expected, testA_out)
-
-        if len(testB_out) > 0:
-            testB_out, testB_expected = preprocess_data(testB_out, testB_expected)
-            if len(testB_out) != len(testB_expected):
-                return ERROR_RESPONSE
-            results["test-B"] = get_scores(testB_expected, testB_out)
-        
-        if len(results) == 0:
-            return ERROR_RESPONSE
-        else:
                 return {
                     "status": 200,
-                "results": results
+                    "result": get_final_score(file_expected, file_out)
                 }
-    elif challenge == "EmotionRecognition":
+        elif metric == "Levenshtein":
-        results = {}
+            if len(file_out) > 0:
-        if len(dev_out) > 0:
+                file_out, file_expected = preprocess_data(file_out, file_expected)
-            df_in = pd.read_table(io.StringIO(dev_in))
+                if len(file_out) != len(file_expected):
-            df_expected = pd.read_table(io.StringIO(dev_expected))
-            df_predition = pd.read_table(io.StringIO(dev_out))
-
-            results["dev-0"] = get_emotion_recognition_scores(df_in, df_expected, df_predition)
-        if len(testA_out) > 0:
-            df_in = pd.read_table(io.StringIO(testA_in))
-            df_expected = pd.read_table(io.StringIO(testA_expected))
-            df_predition = pd.read_table(io.StringIO(testA_out))
-
-            results["test-A"] = get_emotion_recognition_scores(df_in, df_expected, df_predition)
-        if len(testB_out) > 0:
-            df_in = pd.read_table(io.StringIO(testB_in))
-            df_expected = pd.read_table(io.StringIO(testB_expected))
-            df_predition = pd.read_table(io.StringIO(testB_out))
-
-            results["test-B"] = get_emotion_recognition_scores(df_in, df_expected, df_predition)
-        
-        if len(results) == 0:
                     return ERROR_RESPONSE
-        else:
+                
                 return {
                     "status": 200,
-                "results": results
+                    "result": round(get_levenshtein_score(file_expected, file_out), 2)
                 }
-    else:
+        elif metric == "AnswerabilityF1":
+            if len(file_out) > 0:
+                file_out, file_expected = preprocess_data(file_out, file_expected)
+                if len(file_out) != len(file_expected):
+                    return ERROR_RESPONSE
+                
+                return {
+                    "status": 200,
+                    "result": round(get_answerability_f1(file_expected, file_out), 2)
+                }
+        elif metric == "FinalF1":
+            if len(file_out) > 0:
+                df_in = pd.read_table(io.StringIO(file_in))
+                df_expected = pd.read_table(io.StringIO(file_expected))
+                df_predition = pd.read_table(io.StringIO(file_out))
+
+                results = get_emotion_recognition_scores(df_in, df_expected, df_predition)
+
+                return {
+                    "status": 200,
+                    "result": results["FinalF1"]
+                }
+        elif metric == "SentenceF1":
+            if len(file_out) > 0:
+                df_in = pd.read_table(io.StringIO(file_in))
+                df_expected = pd.read_table(io.StringIO(file_expected))
+                df_predition = pd.read_table(io.StringIO(file_out))
+
+                results = get_emotion_recognition_scores(df_in, df_expected, df_predition)
+
+                return {
+                    "status": 200,
+                    "result": results["SentenceF1"]
+                }
+        elif metric == "TextF1":
+            if len(file_out) > 0:
+                df_in = pd.read_table(io.StringIO(file_in))
+                df_expected = pd.read_table(io.StringIO(file_expected))
+                df_predition = pd.read_table(io.StringIO(file_out))
+
+                results = get_emotion_recognition_scores(df_in, df_expected, df_predition)
+
+                return {
+                    "status": 200,
+                    "result": results["TextF1"]
+                }
+    except:
+        return ERROR_RESPONSE
+    
     return ERROR_RESPONSE