Update app.py

app.py

@@ -2,6 +2,8 @@ import os
 import openai
 import gradio as gr
 import time
+import pandas as pd
+import json
 from jtar import jtar_txt
 
 # Set up password
@@ -9,7 +11,7 @@ username = os.getenv('username')
 password = os.getenv('password')
 
 # Set up OpenAI client
-openai_api_key = os.getenv('openai_api_key')
+openai_api_key = os.getenv('OPENAI_API_KEY')
 openai.api_key = openai_api_key
 client = openai.Client(api_key=openai.api_key)
 
@@ -21,38 +23,37 @@ thread = client.beta.threads.create()
 # Get avatar link
 avatar_link = os.getenv('avatar_link')
 
+# Load and prepare the DataFrame
+df = pd.read_json('/content/IB May 2024 Results for Assistant.txt')
+df_json = df.to_json(orient='records')
+
 def chat_with_assistant(message, history):
-    # Add the user's message to the thread
+    # Combine the user's message with the DataFrame
+    combined_message = f"{message}\n\nHere's the DataFrame in JSON format:\n{df_json}"
+    
+    # Add the combined message to the thread
     client.beta.threads.messages.create(
         thread_id=thread.id,
         role="user",
-        content=message
+        content=combined_message
     )
-    
+
     # Run the assistant
     run = client.beta.threads.runs.create(
         thread_id=thread.id,
         assistant_id=assistant_id
     )
-    
+
     # Wait for the assistant's response
     while True:
         run_status = client.beta.threads.runs.retrieve(thread_id=thread.id, run_id=run.id)
         if run_status.status == 'completed':
             # Retrieve the assistant's response
             messages = client.beta.threads.messages.list(thread_id=thread.id)
-            content_block = messages.data[0].content[0]
-            
-            # Check if the response is text or an image
-            if hasattr(content_block, 'text'):
-                assistant_response = content_block.text.value
-            elif hasattr(content_block, 'image_file'):
-                assistant_response = f"<img src='{content_block.image_file.url}' alt='Image' />"
-            else:
-                assistant_response = "[Unknown content type]"
+            assistant_response = messages.data[0].content[0].text.value
             break
         time.sleep(1)
-    
+
     return assistant_response
 
 # Custom CSS for chat bubbles and colors
@@ -63,8 +64,8 @@ custom_css = """
 
 # Create the Gradio interface
 with gr.Blocks(css=custom_css) as demo:
-    gr.HTML(f"""<img src = "https://i.postimg.cc/43kfn8Xx/a-charming-pixar-style-3d-render-of-a-dolphin-in-a-j-GGid5px-R86-Wo-FLn-Re27d-Q-w9-Mz-RVy8-Qti-PQ2-Irspua-LQ.png" width="320" height="240">""")
-    gr.Markdown("# **IB Results Analyst** 📊")
+    gr.HTML(f"""<img src="{avatar_link}" width="320" height="240">""")
+    gr.Markdown("# **IB Results Analyst**📊")
     chatbot = gr.Chatbot(
         [],
         elem_id="chatbot",
@@ -88,6 +89,7 @@ with gr.Blocks(css=custom_css) as demo:
     msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
         bot, chatbot, chatbot
     )
+
     gr.Markdown(jtar_txt)
 
 demo.launch(auth=(username, password))