Update app.py

app.py

@@ -5,7 +5,7 @@ from transformers import pipeline
 from sklearn.metrics.pairwise import cosine_similarity
 import numpy as np
 import os
-from huggingface_hub import upload_file
+from huggingface_hub import upload_file, hf_hub_download
 
 # === Custom PUP-themed CSS ===
 PUP_Themed_css = """
@@ -39,20 +39,33 @@ feedback_embeddings = None
 feedback_path = "outputs/feedback.json"
 os.makedirs("outputs", exist_ok=True)
 
-if os.path.exists(feedback_path) and os.path.getsize(feedback_path) > 0:
-    with open(feedback_path, "r") as f:
-        try:
-            feedback_data = json.load(f)
-            feedback_questions = [item["question"] for item in feedback_data]
-            feedback_answers = [item["response"] for item in feedback_data]
-            if feedback_questions:
-                feedback_embeddings = embedding_model.encode(feedback_questions, convert_to_tensor=True)
-        except json.JSONDecodeError:
-            feedback_data = []
+# === Load feedback from Hugging Face if available ===
+try:
+    hf_token = os.getenv("PUP_AI_Chatbot_Token")
+    downloaded_path = hf_hub_download(
+        repo_id="oceddyyy/University_Inquiries_Feedback",
+        filename="feedback.json",
+        repo_type="dataset",
+        token=hf_token
+    )
+    with open(downloaded_path, "r") as f:
+        feedback_data = json.load(f)
+        feedback_questions = [item["question"] for item in feedback_data]
+        feedback_answers = [item["response"] for item in feedback_data]
+        if feedback_questions:
+            feedback_embeddings = embedding_model.encode(feedback_questions, convert_to_tensor=True)
+
+    # Save to local copy for later editing during runtime
+    with open(feedback_path, "w") as f_local:
+        json.dump(feedback_data, f_local, indent=4)
+
+except Exception as e:
+    print(f"[Startup] No feedback loaded from HF: {e}")
+    feedback_data = []
 
 # === Hugging Face Upload ===
 def upload_feedback_to_hf():
-    hf_token = os.getenv("PUP_AI_Chatbot_Token")  # Access the token from environment variables
+    hf_token = os.getenv("PUP_AI_Chatbot_Token")
     if not hf_token:
         raise ValueError("Hugging Face token not found in environment variables!")
     
@@ -60,7 +73,7 @@ def upload_feedback_to_hf():
         upload_file(
             path_or_fileobj=feedback_path,
             path_in_repo="feedback.json",
-            repo_id="oceddyyy/University_Inquiries_Feedback",  # Replace with your actual HF dataset repo ID
+            repo_id="oceddyyy/University_Inquiries_Feedback",
             repo_type="dataset",
             token=hf_token
         )
@@ -158,7 +171,7 @@ def record_feedback(feedback, chat_history):
         if feedback_questions:
             feedback_embeddings = embedding_model.encode(feedback_questions, convert_to_tensor=True)
 
-        upload_feedback_to_hf()  # Upload feedback to Hugging Face after saving
+        upload_feedback_to_hf()
 
     return gr.update(visible=False)