Update app.py

app.py

@@ -8,17 +8,6 @@ import csv
 from datetime import datetime
 from huggingface_hub import Repository, hf_hub_download
 
-'''
-#Upload File to HF Datasets from https://huggingface.co/docs/huggingface_hub/v0.16.3/en/guides/upload 
-from huggingface_hub import HfApi
-api = HfApi()
-api.upload_file(
-    path_or_fileobj="/path/to/local/folder/README.md",
-    path_in_repo="README.md",
-    repo_id="username/test-dataset",
-    repo_type="dataset",
-)
-'''
 DATASET_REPO_URL = "https://huggingface.co/datasets/botsi/trust-game-llama-2-7b-chat"
 DATASET_REPO_ID = "botsi/trust-game-llama-2-7b-chat"
 DATA_FILENAME = "history_trust-game-llama-2-7b-chat.csv"
@@ -183,99 +172,4 @@ with gr.Blocks() as demo:
         queue=False
     )
 
-demo.launch()
-
-
-
-
-
-
-
-
-    '''def fetch_personalized_data():
-        # Connect to the database
-        conn = mysql.connector.connect(
-            host="18.153.94.89",
-            user="root",
-            password="N12RXMKtKxRj",
-            database="lionessdb"
-        )
-    
-        # Create a cursor object
-        cursor = conn.cursor()
-        
-        # Replace the placeholders with your actual database and table names
-        core_table = "e5390g37096_core"
-        decisions_table = "e5390g37096_decisions"
-        playerNr = "1"
-        
-        # Query to fetch relevant data from both tables based on session_index
-        query = f"""
-            SELECT 
-                {core_table}.playerNr, 
-                {core_table}.subjectNr, 
-                {core_table}.onPage,
-                {decisions_table}.transfer1,
-                {decisions_table}.tripledAmount1,
-                {decisions_table}.keptForSelf1,
-                {decisions_table}.returned1,
-                {decisions_table}.newCreditRound2,
-                {decisions_table}.transfer2,
-                {decisions_table}.tripledAmount2,
-                {decisions_table}.keptForSelf2,
-                {decisions_table}.returned2,
-                {decisions_table}.resultsWithoutAI
-            FROM {core_table}
-            JOIN {decisions_table} ON 
-                {core_table}.playerNr = {decisions_table}.playerNr AND
-                {core_table}.groupNr = {decisions_table}.groupNr AND
-                {core_table}.subjectNr = {decisions_table}.subjectNr
-            WHERE {decisions_table}.session_index = '{session_index}'
-        """
-        cursor.execute(query)
-        
-        # Fetch all rows and convert to a list of dictionaries
-        rows = cursor.fetchall()
-        result = []
-        for row in rows:
-            d = {}
-            for i, col in enumerate(cursor.description):
-                d[col[0]] = row[i]
-            result.append(d)
-    
-        # Convert the list of dictionaries to JSON
-        personalized_result = json.dumps(result)
-    
-        # Close the database connection
-        conn.close()
-        
-        print(personalized_result)
-        
-        # Return the JSON result
-        return personalized_result
-
-    # Call the function to fetch data as JSON
-    # personalized_data = fetch_personalized_data()
-    
-    # Print or use the json_result variable as needed
-    # print(personalized_data)
-    '''
-
-    ''' def bot(history, url_params):
-        global session_index
-        if "read query" in history[-1][0]:
-            session_index = url_params.get('session_index')
-            bot_message = f"""
-            here your URL params:
-            {json.dumps(url_params, indent=4)}
-            """
-        elif "print data" in history[-1][0]:
-            personalized_data = fetch_personalized_data()
-            print(personalized_data)
-            bot_message = "Data printed."
-        else:
-            bot_message = random.choice(["Yes", "No"])
-        history[-1][1] = bot_message
-        time.sleep(1)
-        return history
-'''
+demo.launch()