Update app.py

app.py

@@ -1,164 +1,165 @@
-import os
-import json
-from typing import List, Dict
-
-import streamlit as st
-from streamlit_chat import message
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-
-# Load configuration
-working_dir = os.path.dirname(os.path.abspath(__file__))
-with open(f"{working_dir}/config.json") as config_file:
-    config_data = json.load(config_file)
-OPENAI_API_KEY = config_data["OPENAI_API_KEY"]
-
-# Configure Streamlit page
-st.set_page_config(page_title="Socratic Learning Bot", page_icon="🤖", layout="centered")
-
-# Initialize session state
-if "conversation" not in st.session_state:
-    st.session_state.conversation = None
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = []
-if "hint_level" not in st.session_state:
-    st.session_state.hint_level = 1
-
-
-# Initialize LangChain components
-@st.cache_resource
-def initialize_conversation():
-    llm = ChatOpenAI(
-        temperature=0.7,
-        openai_api_key=OPENAI_API_KEY,
-        model_name="gpt-4"
-    )
-
-    template = """
-    You are an AI tutor designed to help users learn programming concepts and problem-solving skills using a Socratic method. Your primary goal is to guide users towards solutions by asking thought-provoking questions and encouraging critical thinking. Follow these guidelines when responding to user queries:
-
-    1. Begin with questions:
-       a. Ask the user to explain their understanding of the problem.
-       b. Inquire about their current approach or thought process.
-       c. Encourage them to think about potential edge cases or limitations.
-
-    2. If the user provides code, help them identify issues:
-       a. Ask them to walk through their code step-by-step.
-       b. Point out specific lines that may contain errors or inefficiencies.
-       c. Encourage the user to think about why those lines might be problematic.
-
-    3. Provide hints in the following stages, only moving to the next stage if the user is stuck:
-
-       a. Hint 1: Ask a leading question about the topic or sub-topic that guides them towards the solution.
-       b. Hint 2: Provide a conceptual hint framed as a question, encouraging them to make connections.
-       c. Hint 3: Offer a more detailed explanation, but frame it as a series of questions for the user to consider.
-       d. Hint 4: Present pseudocode as a series of questions (e.g., "What if we first...? Then how would we...?").
-       e. Hint 5: Provide a partial code skeleton with key parts missing, asking the user how they would fill in the gaps.
-       f. Hint 6: As a last resort, provide a working solution, but ask the user to explain each part of the code.
-
-    4. Guide users through the problem-solving process from brute force to optimal solutions:
-       a. Ask them to consider the simplest possible solution and its limitations.
-       b. Encourage them to think about how they could improve upon their initial approach.
-       c. Guide them towards optimization by asking about time and space complexity.
-
-    5. Continuously engage the user in the learning process:
-       a. After each response from the user, ask follow-up questions to deepen their understanding.
-       b. Encourage them to predict the outcome of their approach before testing it.
-       c. If they seem confused, ask them to rephrase the problem or explain their current understanding.
-
-    6. Adaptively adjust your questioning:
-       a. If the user is struggling, simplify your questions and provide more context.
-       b. If the user is progressing well, ask more challenging questions to push their understanding further.
-
-    7. Before providing language-specific hints or code:
-       a. Ask the user about their preferred programming language.
-       b. Inquire about their familiarity with relevant language features or libraries.
-
-    Remember, your role is to facilitate active learning. Avoid giving direct answers unless absolutely necessary. Always encourage users to arrive at solutions through their own reasoning and experimentation.
-
-    Current hint level: {hint_level}
-    Recent context:
-    {context}
-
-    Human: {human_input}
-    AI Tutor: Let's approach this step-by-step. First, could you tell me more about...
-    """
-
-    prompt = PromptTemplate(
-        input_variables=["context", "human_input", "hint_level"],
-        template=template
-    )
-
-    return LLMChain(llm=llm, prompt=prompt)
-
-
-# Title
-st.title("🤖 Socratic Learning Bot")
-
-
-# Function to get bot response
-def get_bot_response(user_input: str) -> str:
-    if st.session_state.conversation is None:
-        st.session_state.conversation = initialize_conversation()
-
-    # Include recent chat history for context
-    recent_history = st.session_state.chat_history[-5:]  # Last 5 messages
-    context = "\n".join([f"{msg['role']}: {msg['content']}" for msg in recent_history])
-
-    response = st.session_state.conversation.predict(
-        context=context,
-        human_input=user_input,
-        hint_level=st.session_state.hint_level
-    )
-    return response
-
-
-# Function to display chat history
-def display_chat_history(history: List[Dict[str, str]]):
-    for i, chat in enumerate(history):
-        if chat["role"] == "user":
-            message(chat["content"], is_user=True, key=f"{i}_user")
-        else:
-            message(chat["content"], is_user=False, key=f"{i}_bot")
-
-
-# Main chat interface
-def chat_interface():
-    # Get initial context from the user
-    if not st.session_state.chat_history:
-        context = st.text_input("Please provide the programming topic or problem you'd like help with:")
-        if context:
-            bot_response = get_bot_response(f"The user wants help with the following topic or problem: {context}")
-            st.session_state.chat_history.append({"role": "assistant", "content": bot_response})
-
-    # Display chat history
-    display_chat_history(st.session_state.chat_history)
-
-    # Get user input
-    user_input = st.text_input("Type your message here:", key="user_input")
-
-    # Add buttons for user actions
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        if st.button("Send"):
-            if user_input:
-                st.session_state.chat_history.append({"role": "user", "content": user_input})
-                bot_response = get_bot_response(user_input)
-                st.session_state.chat_history.append({"role": "assistant", "content": bot_response})
-                st.rerun()
-    with col2:
-        if st.button("Next Hint"):
-            st.session_state.hint_level = min(st.session_state.hint_level + 1, 6)
-            bot_response = get_bot_response("I need more help. Can you provide the next hint?")
-            st.session_state.chat_history.append({"role": "assistant", "content": bot_response})
-            st.rerun()
-    with col3:
-        if st.button("Reset Hints"):
-            st.session_state.hint_level = 1
-            st.session_state.chat_history.append({"role": "system", "content": "Hint level has been reset to 1."})
-            st.rerun()
-
-
-if __name__ == "__main__":
+import os
+import json
+from typing import List, Dict
+
+import streamlit as st
+from streamlit_chat import message
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
+
+# Load configuration
+# working_dir = os.path.dirname(os.path.abspath(__file__))
+# with open(f"{working_dir}/config.json") as config_file:
+#     config_data = json.load(config_file)
+# OPENAI_API_KEY = config_data["OPENAI_API_KEY"]
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Configure Streamlit page
+st.set_page_config(page_title="Socratic Learning Bot", page_icon="🤖", layout="centered")
+
+# Initialize session state
+if "conversation" not in st.session_state:
+    st.session_state.conversation = None
+if "chat_history" not in st.session_state:
+    st.session_state.chat_history = []
+if "hint_level" not in st.session_state:
+    st.session_state.hint_level = 1
+
+
+# Initialize LangChain components
+@st.cache_resource
+def initialize_conversation():
+    llm = ChatOpenAI(
+        temperature=0.7,
+        openai_api_key=OPENAI_API_KEY,
+        model_name="gpt-4"
+    )
+
+    template = """
+    You are an AI tutor designed to help users learn programming concepts and problem-solving skills using a Socratic method. Your primary goal is to guide users towards solutions by asking thought-provoking questions and encouraging critical thinking. Follow these guidelines when responding to user queries:
+
+    1. Begin with questions:
+       a. Ask the user to explain their understanding of the problem.
+       b. Inquire about their current approach or thought process.
+       c. Encourage them to think about potential edge cases or limitations.
+
+    2. If the user provides code, help them identify issues:
+       a. Ask them to walk through their code step-by-step.
+       b. Point out specific lines that may contain errors or inefficiencies.
+       c. Encourage the user to think about why those lines might be problematic.
+
+    3. Provide hints in the following stages, only moving to the next stage if the user is stuck:
+
+       a. Hint 1: Ask a leading question about the topic or sub-topic that guides them towards the solution.
+       b. Hint 2: Provide a conceptual hint framed as a question, encouraging them to make connections.
+       c. Hint 3: Offer a more detailed explanation, but frame it as a series of questions for the user to consider.
+       d. Hint 4: Present pseudocode as a series of questions (e.g., "What if we first...? Then how would we...?").
+       e. Hint 5: Provide a partial code skeleton with key parts missing, asking the user how they would fill in the gaps.
+       f. Hint 6: As a last resort, provide a working solution, but ask the user to explain each part of the code.
+
+    4. Guide users through the problem-solving process from brute force to optimal solutions:
+       a. Ask them to consider the simplest possible solution and its limitations.
+       b. Encourage them to think about how they could improve upon their initial approach.
+       c. Guide them towards optimization by asking about time and space complexity.
+
+    5. Continuously engage the user in the learning process:
+       a. After each response from the user, ask follow-up questions to deepen their understanding.
+       b. Encourage them to predict the outcome of their approach before testing it.
+       c. If they seem confused, ask them to rephrase the problem or explain their current understanding.
+
+    6. Adaptively adjust your questioning:
+       a. If the user is struggling, simplify your questions and provide more context.
+       b. If the user is progressing well, ask more challenging questions to push their understanding further.
+
+    7. Before providing language-specific hints or code:
+       a. Ask the user about their preferred programming language.
+       b. Inquire about their familiarity with relevant language features or libraries.
+
+    Remember, your role is to facilitate active learning. Avoid giving direct answers unless absolutely necessary. Always encourage users to arrive at solutions through their own reasoning and experimentation.
+
+    Current hint level: {hint_level}
+    Recent context:
+    {context}
+
+    Human: {human_input}
+    AI Tutor: Let's approach this step-by-step. First, could you tell me more about...
+    """
+
+    prompt = PromptTemplate(
+        input_variables=["context", "human_input", "hint_level"],
+        template=template
+    )
+
+    return LLMChain(llm=llm, prompt=prompt)
+
+
+# Title
+st.title("🤖 Socratic Learning Bot")
+
+
+# Function to get bot response
+def get_bot_response(user_input: str) -> str:
+    if st.session_state.conversation is None:
+        st.session_state.conversation = initialize_conversation()
+
+    # Include recent chat history for context
+    recent_history = st.session_state.chat_history[-5:]  # Last 5 messages
+    context = "\n".join([f"{msg['role']}: {msg['content']}" for msg in recent_history])
+
+    response = st.session_state.conversation.predict(
+        context=context,
+        human_input=user_input,
+        hint_level=st.session_state.hint_level
+    )
+    return response
+
+
+# Function to display chat history
+def display_chat_history(history: List[Dict[str, str]]):
+    for i, chat in enumerate(history):
+        if chat["role"] == "user":
+            message(chat["content"], is_user=True, key=f"{i}_user")
+        else:
+            message(chat["content"], is_user=False, key=f"{i}_bot")
+
+
+# Main chat interface
+def chat_interface():
+    # Get initial context from the user
+    if not st.session_state.chat_history:
+        context = st.text_input("Please provide the programming topic or problem you'd like help with:")
+        if context:
+            bot_response = get_bot_response(f"The user wants help with the following topic or problem: {context}")
+            st.session_state.chat_history.append({"role": "assistant", "content": bot_response})
+
+    # Display chat history
+    display_chat_history(st.session_state.chat_history)
+
+    # Get user input
+    user_input = st.text_input("Type your message here:", key="user_input")
+
+    # Add buttons for user actions
+    col1, col2, col3 = st.columns(3)
+    with col1:
+        if st.button("Send"):
+            if user_input:
+                st.session_state.chat_history.append({"role": "user", "content": user_input})
+                bot_response = get_bot_response(user_input)
+                st.session_state.chat_history.append({"role": "assistant", "content": bot_response})
+                st.rerun()
+    with col2:
+        if st.button("Next Hint"):
+            st.session_state.hint_level = min(st.session_state.hint_level + 1, 6)
+            bot_response = get_bot_response("I need more help. Can you provide the next hint?")
+            st.session_state.chat_history.append({"role": "assistant", "content": bot_response})
+            st.rerun()
+    with col3:
+        if st.button("Reset Hints"):
+            st.session_state.hint_level = 1
+            st.session_state.chat_history.append({"role": "system", "content": "Hint level has been reset to 1."})
+            st.rerun()
+
+
+if __name__ == "__main__":
     chat_interface()