add new return format

evaluator.py

@@ -181,14 +181,17 @@ def evaluate_interview(competences: list[str], transcript: list):
                 What are your nightmare?
 
                 INTERVIEWEE:
-                I Do not have night mare
-                OUTPUT: FAIL
+                I do not have night mare
 
-                Always send output in format "FAIL" or "SUCCESS"
+                Judgement: It is impossible to some not having any nightmare. Scary of something is common human feels.
+                Score: 0.1
 
                 RETURN IN FORMAT BELOW:
                 {
-                value: [{"label": "SUCCESS"},{"label": "FAIL"}]
+                value: [{
+                    "Judgement": "It is impossible to some not having any nightmare. Scary of something is common human feels. Means he was lying",
+                    "score": 0.1
+                    }]
                 }
                 """, 
                 list(Dict[str,str])
@@ -224,23 +227,7 @@ def generate_behavioral_score(eval_array):
     scores = []
 
     for eval in eval_array:
-        fail_score = 0
-        success_score = 0
-
-        if eval.label == "FAIL":
-            fail_score += 1
-        elif eval.label == "SUCCESS":
-            success_score += 1 
-
-        # if eval.label == "FAIL":
-        #     fail_score = eval["score"]
-        # elif eval.label == "SUCCESS":
-        #     success_score = eval["score"]
-
-        if fail_score < success_score:
-            scores.append(1)
-        else:
-            scores.append(0)
+        scores.append(eval.score)
     
     return scores
 

extractor_llm.py

@@ -29,7 +29,7 @@ def predict(input):
     below is the example:
 
     {
-    "name": "MUHAMMAD RISQI FIRDAUS",
+    "name": "Faiq Bil Haq Izzuddin",
     "skills": [
         "Python (Matplotlib, Pandas)",
         "Seaborn,",
@@ -59,10 +59,10 @@ def predict(input):
         "PySpark."
     ],
     "links": [
-        "linkedin.com/in/mrfirdauss/",
-        "github.com/mrfirdauss-20",
-        "mrfirdauss.vercel.app",
-        "kaggle.com/mrfirdauss20"
+        "linkedin.com/in/faiz-b-h/",
+        "github.com/mfaizbh22",
+        "faizzz.vercel.app",
+        "kaggle.com/mfaizb"
     ],
     "experiences": [
         {
@@ -105,6 +105,7 @@ def predict(input):
     ]
     }
     \n
+    If the document OCR read or extraction is null, please return with empty structue. 
     """
     model_parameters  = client.beta.chat.completions.parse(
         model="gpt-4o-2024-08-06",

models.py

@@ -61,11 +61,16 @@ class InsertedLink(BaseModel):
     link: str
 
 class Evaluation(BaseModel):
-    label: str = Field(None, description="Fill with FAIL or SUCCESS")
+    score: float = Field(None, description="Float range between [0,1]")
+    judgement: str = Field(None, description="Give judgement about the score of the interview evaluation.")
 
 class Evaluations(BaseModel):
     value: list[Evaluation] = Field(...)
 
 class EvaModul(BaseModel):
     competences: list[str]
-    transcript: list[list[Dict[str,str]]]
+    transcript: list[list[Dict[str,str]]]
+
+class EvalResult(BaseModel):
+    final_score: float
+    behavioural: Evaluations