Update metric.py

metric.py

@@ -1,18 +1,46 @@
 import openai
 openai.api_key = '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'
 
-def compute_metric(params):
+import pandas as pd
+from huggingface_hub import hf_hub_download
+
+def compute(params):
     public_score = 0
     private_score = 0
+
+    solution_file = hf_hub_download(
+        repo_id=params.competition_id,
+        filename="solution.csv",
+        token=params.token,
+        repo_type="dataset",
+    )
+
+    solution_df = pd.read_csv(solution_file)
+
+    submission_filename = f"submissions/{params.team_id}-{params.submission_id}.csv"
+    submission_file = hf_hub_download(
+        repo_id=params.competition_id,
+        filename=submission_filename,
+        token=params.token,
+        repo_type="dataset",
+    )
+    submission_df = pd.read_csv(submission_file)
+
+    submitted_answer = str(submission_df.iloc[0]['pred'])
+    gt = str(solution_df.iloc[0]['pred'])
+
+    prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {submitted_answer}. Caption two:
+    {gt}\nScore:"
     
     response = openai.Completion.create(
       engine="text-davinci-003",
-      prompt="Rank the following statement on accuracy, with 10 being the most accurate and 0 the least: 'World War 2 ended in 1954.'\nScore:",
+      prompt=prompt,
       temperature=0,
       max_tokens=1,
     )
 
     public_score = int(response.choices[0].text.strip())
+    private_score = public_score
     
     metric_dict = {
     "public_score": {"metric1": public_score},