Code Changes - Results Upload

app.py

@@ -82,7 +82,8 @@ def run_evaluation(
                 data = json.loads(line)
                 useddata[data['id']] = data
 
-    print(f"********Information about usedata: {useddata}")
+
+    # print(f"********Information about usedata: {useddata}")
 
     # Inference loop
     results = []
@@ -105,7 +106,7 @@ def run_evaluation(
                     # print(f"Results: \n*********query: {query}, \n*********Answer: {ans}, \n*********docs: {docs}\n*********\n")
                 
                 label, prediction, factlabel = predict(query, ans, docs, model, system, instruction, temperature, dataset)
-                print("f******** Label: {label}\n******** Prediction: {prediction}\n******** factlabel: {factlabel}\n ******** \n")
+                print(f"******** Label: {label}\n******** Prediction: {prediction}\n******** factlabel: {factlabel}\n ******** \n")
                 
                 newinstance = {
                     'id': instance['id'],
@@ -117,7 +118,7 @@ def run_evaluation(
                     'noise_rate': noise_rate,
                     'factlabel': factlabel
                 }
-                print(f"*********Newinstances: {newinstance}")
+                # print(f"*********Newinstances: {newinstance}")
                 results.append(newinstance)
                 f.write(json.dumps(newinstance, ensure_ascii=False) + '\n')
             except Exception as e:
@@ -157,11 +158,23 @@ def run_evaluation(
             'correct_tt': correct_tt
         })
 
+    # Upload results to Hugging Face Hub
+    try:
+        upload_file = model.upload_file(output_file, resultpath)
+        if upload_file:
+            print(f"File {output_file} uploaded successfully to Hugging Face Hub.") 
+        else:
+            print(f"Failed to upload {output_file} to Hugging Face Hub.")
+    except Exception as e:
+        print(f"Error uploading file: {e}")
+
     # Save results
     try:
         score_file = f"{output_file[:-5]}_result.json"
         with open(score_file, 'w') as f:
             json.dump(scores, f, ensure_ascii=False, indent=4)
+        model.upload_file(score_file, resultpath)
+        print(f"Scores saved to {score_file} and uploaded to Hugging Face Hub.")
     except Exception as e:
         print("Error saving scores:", e)
 

constants.py

@@ -0,0 +1,3 @@
+
+HF_DATASET_REPO_NAME = "maddiaks/RGB26Demo"
+HF_REPO_TYPE = "space"

utils.py

@@ -1,5 +1,11 @@
 import random
 import math
+import json
+import numpy as np
+import os
+from huggingface_hub import HfApi
+from constants import HF_DATASET_REPO_NAME, HF_REPO_TYPE
+
 
 def processdata(instance, noise_rate, passage_num, filename, correct_rate = 0):
     query = instance['query']
@@ -115,4 +121,24 @@ def predict(query, ground_truth, docs, model, system, instruction, temperature,
     if '事实性错误' in prediction or 'factual errors' in prediction:
         factlabel = 1
 
-    return labels,prediction, factlabel
+    return labels,prediction, factlabel
+
+def upload_file(filename: str, folder_path: str) -> str:
+    """Upload a file to Hugging Face hub from the specified folder."""
+    try:
+        # file_path = os.path.join(folder_path, filename)
+        # if not os.path.exists(file_path):
+        #     raise FileNotFoundError(f"File {file_path} does not exist.")
+        
+        api = HfApi()
+        api.upload_file(
+            path_or_fileobj=filename,
+            path_in_repo=f"{folder_path}/{filename}",
+            repo_id=HF_DATASET_REPO_NAME,
+            repo_type=HF_REPO_TYPE,
+            token=os.getenv("HF_TOKEN")
+        )
+        print(f"Uploaded {filename} to {HF_DATASET_REPO_NAME}")
+    except Exception as e:
+        print(f"Error uploading {filename}: {e}")
+        return None