Update app.py

app.py

@@ -1,5 +1,7 @@
-import gradio as gr
 import os
+import openai
+import gradio as gr
+import time
 from langchain_openai import ChatOpenAI
 from langchain.agents import AgentExecutor, create_tool_calling_agent
 from langchain_community.tools.tavily_search import TavilySearchResults
@@ -7,16 +9,21 @@ from langchain_core.prompts import ChatPromptTemplate
 from datetime import datetime, timedelta
 
 # Set up API keys
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
-os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-os.environ["TAVILY_API_KEY"] = TAVILY_API_KEY
+openai.api_key = os.getenv('OPENAI_API_KEY')
+TAVILY_API_KEY = os.getenv('TAVILY_API_KEY')
+assistant_id = os.getenv('ASSISTANT_ID')
+username = os.getenv('USERNAME')
+password = os.getenv('PASSWORD')
 
-# Initialize the LLM
-llm = ChatOpenAI(model="gpt-4o-mini")
+client = openai.Client(api_key=openai.api_key)
+assistant = client.beta.assistants.retrieve(assistant_id)
+thread = client.beta.threads.create()
+
+# Initialize the LLM for article generation
+llm_article = ChatOpenAI(model="gpt-4o-mini")
 
 # Set Up the Tavily Search Tool
-tools = [TavilySearchResults(max_results=3)]
+tools = [TavilySearchResults(max_results=4)]
 
 # CEFR levels and their descriptions
 CEFR_LEVELS = {
@@ -30,24 +37,24 @@ CEFR_LEVELS = {
 }
 
 # Create a Chat Prompt Template
-prompt = ChatPromptTemplate.from_messages([
+prompt_article = ChatPromptTemplate.from_messages([
     ("system", "You are a helpful assistant for ESL learners. Use the tavily_search_results_json tool to find current news information about the students' interests."),
     ("human", "{input}"),
     ("placeholder", "{agent_scratchpad}"),
 ])
 
 # Construct the Tools agent
-agent = create_tool_calling_agent(llm, tools, prompt)
+agent_article = create_tool_calling_agent(llm_article, tools, prompt_article)
 
 # Create an agent executor
-agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+agent_executor_article = AgentExecutor(agent=agent_article, tools=tools, verbose=True)
 
 def get_news_content(topic):
     current_date = datetime.now().strftime("%Y-%m-%d")
     week_ago = (datetime.now() - timedelta(days=7)).strftime("%Y-%m-%d")
     query = f"Summarize the latest news about {topic} from {week_ago} to {current_date}"
     
-    result = agent_executor.invoke({"input": query})
+    result = agent_executor_article.invoke({"input": query})
     return result['output'], result.get('intermediate_steps', [])
 
 def generate_article(topic, content, urls, level, learning_objective=None):
@@ -59,7 +66,7 @@ def generate_article(topic, content, urls, level, learning_objective=None):
     The article should be engaging, informative, and appropriate for the specified language level.
     Include a 'Sources' section at the end of the article with the following URLs: {urls}
     """
-    result = agent_executor.invoke({"input": prompt})
+    result = agent_executor_article.invoke({"input": prompt})
     return result['output']
 
 def extract_language_elements(article, level, learning_objective=None):
@@ -77,7 +84,7 @@ def extract_language_elements(article, level, learning_objective=None):
     Return the results in a structured format.
     Article: {article}
     """
-    result = agent_executor.invoke({"input": prompt})
+    result = agent_executor_article.invoke({"input": prompt})
     return result['output']
 
 def generate_feedback_and_takeaway(article, level, language_elements, learning_objective=None):
@@ -95,40 +102,7 @@ def generate_feedback_and_takeaway(article, level, language_elements, learning_o
     Article: {article}
     Language Elements: {language_elements}
     """
-    result = agent_executor.invoke({"input": prompt})
-    return result['output']
-
-def generate_response(user_input, context, level, learning_objective=None):
-    if level is None:
-        return "Please specify your English proficiency level."
-    if level not in CEFR_LEVELS:
-        return "Invalid English proficiency level specified."
-    
-    objective_text = f"Additionally, focus on the following learning objective: {learning_objective}. " if learning_objective else ""
-    prompt = f"""
-    #Role
-    You are an ESL teacher
-    You answer students' questions about an article, helping them understand it, with a focus on language elements.
-    You also ask the student questions aligned with relevant language elements to check their understanding.
-    #Instructions
-    1. If the student asks a question: Based on the following article for a {level} ({CEFR_LEVELS[level]}) English language learner, respond to the user's input in a helpful manner.
-    2. When the user is ready, ask relevant questions to check for their understanding of the article, but also to practice the relevant language elements.
-    Ensure questions are challenging but appropriate for the {level} level.
-    Avoid questions with obvious answers.
-    Types of questions should include:
-    - Multiple-choice questions
-    - True/false questions 
-    - Fill-in-the-blank questions 
-    - Questions to apply the elements of language learned in new contexts
-    - Open-ended questions
-    Questions should become increasingly difficult as the student succeeds, and easier if they struggle.
-    When the student fails, provide a hint. If they fail again, provide the answer and an explanation.
-    {objective_text}
-    Article: {context['article']}
-    Language Elements: {context['language_elements']}
-    User Input: {user_input}
-    """
-    result = agent_executor.invoke({"input": prompt})
+    result = agent_executor_article.invoke({"input": prompt})
     return result['output']
 
 def main(topic, level, learning_objective=None):
@@ -142,7 +116,9 @@ def main(topic, level, learning_objective=None):
             context = {
                 'article': article,
                 'language_elements': language_elements,
-                'feedback_and_takeaway': feedback_and_takeaway
+                'feedback_and_takeaway': feedback_and_takeaway,
+                'level': level,
+                'learning_objective': learning_objective
             }
             return article, feedback_and_takeaway, context
         else:
@@ -150,9 +126,40 @@ def main(topic, level, learning_objective=None):
     except Exception as e:
         return f"An error occurred: {str(e)}", "", None
 
-def chat(user_input, context, level, learning_objective):
-    response = generate_response(user_input, context, level, learning_objective)
-    return [(user_input, response)]
+# Function to handle chat interaction with the existing OpenAI assistant
+def chat_with_assistant(message, context):
+    if context is None:
+        return [("System", "Please generate an article first.")]
+
+    level = context['level']
+    article = context['article']
+    language_elements = context['language_elements']
+    learning_objective = context.get('learning_objective', '')
+
+    # Add the user's message to the thread
+    client.beta.threads.messages.create(
+        thread_id=thread.id,
+        role="user",
+        content=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)
+            assistant_response = messages.data[-1].content[0].text.value
+            break
+        time.sleep(1)
+    
+    return [(message, assistant_response)]
 
 # Gradio Interface
 with gr.Blocks() as demo:
@@ -172,16 +179,15 @@ with gr.Blocks() as demo:
         context.value = ctx
         return article, feedback
 
-    generate_btn.click(on_generate, inputs=[topic_input, level_input, objective_input], outputs=[article_output, feedback_output])
-
     def on_chat(user_input):
         if context.value is not None:
-            response = chat(user_input, context.value, level_input.value, objective_input.value)
+            response = chat_with_assistant(user_input, context.value)
             return response
         else:
-            return "Please generate an article first."
+            return [("System", "Please generate an article first.")]
 
+    generate_btn.click(on_generate, inputs=[topic_input, level_input, objective_input], outputs=[article_output, feedback_output])
     chat_input.submit(on_chat, inputs=[chat_input], outputs=[chat_output])
 
-demo.launch()
+demo.launch(auth=(username, password))