Update app.py

app.py

@@ -2,10 +2,13 @@ from gpt_index import GPTSimpleVectorIndex
 from langchain import OpenAI
 import gradio as gr
 from gradio import Interface, Textbox
+import sys
 import os
 import datetime
-from datasets import load_dataset
-from huggingface_hub import HfFolder
+import huggingface_hub
+from huggingface_hub import Repository
+from datetime import datetime
+import csv
 
 os.environ["OPENAI_API_KEY"] = os.environ['SECRET_CODE']
 
@@ -19,15 +22,19 @@ INDEX_FILE = os.path.join("data", INDEX_FILENAME)
 # we need a write access token.
 HF_TOKEN = os.environ.get("HF_TOKEN")
 print("HF TOKEN is none?", HF_TOKEN is None)
+print("HF hub ver", huggingface_hub.__version__)
 
-# Clones the distant repo to the local repo
-dataset = load_dataset(DATASET_REPO_URL)
-dataset_folder = HfFolder(dataset._data_files["train"][0].path).path
-print(f"Dataset folder: {dataset_folder}")
-print(f"Dataset files: {os.listdir(dataset_folder)}")
+#Clones the distant repo to the local repo
+repo = Repository(
+    local_dir='data', 
+    clone_from=DATASET_REPO_URL, 
+    use_auth_token=HF_TOKEN)
+
+print(f"Repo local_dir: {repo.local_dir}")
+print(f"Repo files: {os.listdir(repo.local_dir)}")
 
 def generate_text() -> str:
-    with open(os.path.join(dataset_folder, DATA_FILENAME)) as file:
+    with open(DATA_FILE) as file:
         text = ""
         for line in file:
             row_parts = line.strip().split(",")
@@ -40,21 +47,24 @@ def generate_text() -> str:
 
 def store_message(chatinput: str, chatresponse: str):
     if chatinput and chatresponse:
-        with open(os.path.join(dataset_folder, DATA_FILENAME), "a") as file:
-            file.write(f"{datetime.datetime.now()},{chatinput},{chatresponse}\n")
-            print(f"Wrote to datafile: {datetime.datetime.now()},{chatinput},{chatresponse}\n")
-
-        # Push back to hub every N-th time the function is called
-        if store_message.count_calls % 1 == 0:
-            print("Pushing back to Hugging Face model hub")
-            dataset.commit("Added new chat data")  # Commit the changes
-        store_message.count_calls += 1
+        with open(DATA_FILE, "a") as file:
+            file.write(f"{datetime.now()},{chatinput},{chatresponse}\n")
+            print(f"Wrote to datafile: {datetime.now()},{chatinput},{chatresponse}\n")
+
+        # Add the updated data file to the staged changes
+        repo.git_add(DATA_FILENAME)
+
+        # Commit the changes with a commit message
+        commit_message = "Added new data"
+        repo.git_commit(commit_message)
+
+        # Push the changes to the remote repository
+        repo.push(remote_name='origin', branch_name='main', use_auth_token=HF_TOKEN)        
         
         return generate_text()
 
-store_message.count_calls = 1  #initiases the count at one. We want to count how many messages stored before pushing back to repo.
-
-# gets the index file which is the context data
+        
+#gets the index file which is the context data
 def get_index(index_file_path):
     if os.path.exists(index_file_path):
         index_size = os.path.getsize(index_file_path)
@@ -82,7 +92,7 @@ with open('about.txt', 'r') as file:
 
 iface = Interface(
     fn=chatbot,
-    inputs=Textbox("Enter your question"),
+    inputs=Textbox("Type here"),
     outputs="text",
     title="AI Chatbot trained on J. Haynes mediation material, v0.5",
     description=about)