agentic update

app.py

@@ -4,18 +4,13 @@ import streamlit as st
 from openai import OpenAI
 from dotenv import load_dotenv
 
-# For OCR
-import pytesseract
-from PIL import Image
-
-# Set page to wide layout so you can see everything
-st.set_page_config(layout="wide")
-
 load_dotenv()
-
-# Initialize OpenAI client
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
+# --------------------------------------------
+# Core Functions
+# --------------------------------------------
+
 def default_prompt_builder(
     math_subject: str,
     topic: str,
@@ -27,7 +22,7 @@ def default_prompt_builder(
     prev_lecture: str
 ) -> str:
     """
-    Builds the default prompt based on the user inputs.
+    Builds the base lecture prompt.
     """
     return f"""
 You are a math teacher. Create a comprehensive lecture script for a lesson in "{math_subject}" on the topic "{topic}"
@@ -44,44 +39,33 @@ The script must be fluent with smooth transitions and detailed explanations of t
 2. **Topic Intro :**
    Present a concise background or interesting fact about the topic.
 
-3. **Core Concept Definition:**
+3. **Core Concept Definition :**
    Provide a clear definition of the core concepts. Explain their importance and mention any
    fundamental equations, theorems, or limit definitions that underlie them. Include mathematical equations if necessary.
 
-4. **Detailed Explanation:**
+4. **Detailed Explanation :**
    Offer a detailed, step-by-step explanation of the topic. Incorporate deeper theoretical underpinnings—
    such as derivations from first principles or explanations of common misconceptions and how to avoid them.
 
-5. **Example Problem:**
-   Use the following example problem for a thorough walkthrough and please add transitions so the script is fluent:
+5. **Example Problem :**
+   Use the following example problem for a thorough walkthrough:
    "{example1}"
-     - Provide a complete, step-by-step solution. Name it as step 1, step 2, etc.
-     - Write out the mathematical equations.
+     - Provide a step-by-step solution (e.g., Step1, Step2, Step3, etc.).
      - Reference relevant theorems or limit definitions.
-     - Provide common mistakes with transitions and demonstrate how to correct them.
-     - Mention alternative methods or shortcuts where relevant with transitions.
-     - Clearly summarize the final result.
+     - Mention common mistakes with transitions and how to correct them.
+     - Summarize the final result clearly.
 
-6. **Problem 1:**
+6. **Problem 1 :**
    Use the following second problem:
    "{problem1}"
-   Provide a complete, detailed solution, following the same guidelines and use transitions between each step:
-     - Step-by-step solution with numbered reasoning. Name it as step 1, step 2, etc.
-     - Reference relevant theorems/definitions.
-     - Write out the mathematical equations.
-     - Summarize the final result.
+   Provide a detailed solution with numbered steps and transitions, referencing theorems/definitions, and summarizing the result.
 
-7. **Word Problem:**
+7. **Word Problem :**
    Use the following word problem:
    "{wordproblem}"
-   Solve it thoroughly and use transitions between each step:
-     - Walk through each step in detail with numbered guidance. Name it as step 1, step 2, etc.
-     - Reference key theorems.
-     - Write out the mathematical equations.
-     - Using transitions, naturally add common pitfalls and offer alternative approaches.
-     - Provide a clear final summary.
-
-8. **Engagement, Reinforcement, and Conclusion:**
+   Solve it thoroughly with numbered steps and transitions, referencing key theorems, discussing pitfalls, and offering alternative approaches.
+
+8. **Engagement, Reinforcement, and Conclusion :**
    Summarize the key points and offer additional tips or alternative approaches for deeper understanding.
    End with a motivational wrap-up, leaving the audience with a final thought or question.
 
@@ -89,7 +73,9 @@ Begin your response now.
 """
 
 def call_openai_chat(prompt: str, max_tokens: int = 2000, temperature: float = 0.4) -> str:
-    """Generic function to call OpenAI chat completions."""
+    """
+    Calls the OpenAI chat completions API with the provided prompt.
+    """
     response = client.chat.completions.create(
         model="gpt-4o-2024-08-06",
         messages=[{"role": "user", "content": prompt}],
@@ -98,159 +84,209 @@ def call_openai_chat(prompt: str, max_tokens: int = 2000, temperature: float = 0
     )
     return response.choices[0].message.content.strip()
 
-def unify_all_math_as_double_dollars(text: str) -> str:
+def check_transitions(lecture_script: str) -> str:
+    prompt = f"""
+Review the following lecture script and identify any sections that lack smooth transitions. 
+Point out where transitions are missing and suggest improvements.
+
+Lecture Script:
+\"\"\"{lecture_script}\"\"\"
+    
+Return only your feedback and suggestions.
+"""
+    return call_openai_chat(prompt)
+
+def check_errors(lecture_script: str) -> str:
+    prompt = f"""
+Review the following lecture script for any errors including grammar, mathematical inaccuracies, or formatting issues. 
+List the errors and provide suggestions for corrections.
+
+Lecture Script:
+\"\"\"{lecture_script}\"\"\"
+    
+Return only your feedback and suggestions.
+"""
+    return call_openai_chat(prompt)
+
+def check_step_organization(lecture_script: str) -> str:
+    prompt = f"""
+Analyze the following lecture script and check whether all problem solutions have their steps clearly enumerated (e.g., Step1, Step2, Step3, etc.). 
+If any solutions are missing this clear organization, list those sections and suggest improvements.
+
+Lecture Script:
+\"\"\"{lecture_script}\"\"\"
+    
+Return only your feedback and suggestions.
+"""
+    return call_openai_chat(prompt)
+
+def refine_lecture_manual(lecture_script: str, transitions_feedback: str, errors_feedback: str, steps_feedback: str) -> str:
     """
-    Ensures that all LaTeX math (whether in $...$, $$...$$, \( ... \), or \[ ... \])
-    is converted into double-dollar $$...$$ blocks.
+    Produces a refined version of the lecture script using agent feedback.
+    This version may include a separate **Common Mistakes:** section.
     """
-    text = re.sub(r'\$\$(.+?)\$\$', r'__DOUBLE__PLACEHOLDER__\1__DOUBLE__PLACEHOLDER__', text, flags=re.DOTALL)
-    text = re.sub(r'\\\[(.+?)\\\]', r'__DBRACK__PLACEHOLDER__\1__DBRACK__PLACEHOLDER__', text, flags=re.DOTALL)
-    text = re.sub(r'\\\((.+?)\\\)', r'__DBPAREN__PLACEHOLDER__\1__DBPAREN__PLACEHOLDER__', text, flags=re.DOTALL)
-    text = re.sub(r'\$(.+?)\$', r'__SINGLE__PLACEHOLDER__\1__SINGLE__PLACEHOLDER__', text, flags=re.DOTALL)
-    text = re.sub(r'__DOUBLE__PLACEHOLDER__(.+?)__DOUBLE__PLACEHOLDER__', r'$$\1$$', text, flags=re.DOTALL)
-    text = re.sub(r'__DBRACK__PLACEHOLDER__(.+?)__DBRACK__PLACEHOLDER__', r'$$\1$$', text, flags=re.DOTALL)
-    text = re.sub(r'__DBPAREN__PLACEHOLDER__(.+?)__DBPAREN__PLACEHOLDER__', r'$$\1$$', text, flags=re.DOTALL)
-    text = re.sub(r'__SINGLE__PLACEHOLDER__(.+?)__SINGLE__PLACEHOLDER__', r'$$\1$$', text, flags=re.DOTALL)
-    return text
-
-def ask_agent_for_improvement(script_text: str, user_request: str, max_tokens: int = 1500, temperature: float = 0.5) -> str:
+    prompt = f"""
+You are an expert in educational content design. Below is the original lecture script along with feedback from three specialized agents:
+
+--- Original Lecture Script ---
+\"\"\"{lecture_script}\"\"\"
+
+--- Feedback ---
+Transitions Feedback:
+{transitions_feedback}
+
+Errors Feedback:
+{errors_feedback}
+
+Step Organization Feedback:
+{steps_feedback}
+
+Using this feedback, provide a refined version of the lecture script that addresses all the issues mentioned. 
+Ensure that transitions between sections are smooth, all errors are corrected, and all problem solutions have clearly enumerated steps.
+Include a section labeled **Common Mistakes:** if necessary.
+Return only the refined lecture script.
+"""
+    return call_openai_chat(prompt)
+
+def add_extra_transitions(lecture_script: str) -> str:
     """
-    Sends the existing lecture script plus the user's request
-    to GPT, allowing the user to ask for improvements, expansions,
-    or fluidity checks.
+    Enhances the refined lecture script by integrating transitions, summaries, and commentary on common mistakes seamlessly into the narrative.
+    This function checks if the refined script contains integrated commentary on common mistakes (e.g., "Common Mistake:" or "**Pitfall:**")
+    and summary insights (e.g., "Summary:"). If such elements are missing or not well integrated, it weaves them naturally into the text.
     """
     prompt = f"""
-You have the following lecture script:
+You are provided with a refined lecture script. Your task is to enhance it by:
+1. Adding explicit transitions between steps and sections.
+2. Seamlessly integrating key conclusions, summary insights, and commentary on common mistakes directly into the narrative.
+3. Checking whether the lecture script includes integrated remarks on common mistakes (e.g., "Common Mistake:" or "**Pitfall:**") and summaries (e.g., "Summary:"); if not, embed these elements naturally into the content.
+4. Avoid appending these as separate sections—instead, ensure the final output reads as a unified, continuous lecture script.
 
-{script_text}
+Return only the enhanced lecture script.
 
-A user requests the following improvement or check:
-"{user_request}"
-
-Please provide a helpful rewrite that addresses the user's request while preserving all essential details.
+Lecture Script:
+\"\"\"{lecture_script}\"\"\"
 """
-    response = client.chat.completions.create(
-        model="gpt-4o-2024-08-06",
-        messages=[
-            {"role": "system", "content": "You are an expert academic editor specialized in lecture scripts."},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=max_tokens,
-        temperature=temperature
-    )
-    return response.choices[0].message.content.strip()
+    return call_openai_chat(prompt)
 
-# ------------------ Streamlit UI ------------------
+# --------------------------------------------
+# Sidebar: Input Parameters
+# --------------------------------------------
 
-st.title("Lecture Script Generator with OCR and Manual Prompt Editing")
+st.sidebar.header("Input Parameters")
 
-# Sidebar: Input settings
-st.sidebar.header("Input Settings")
-math_subject = st.sidebar.text_input("Math Subject", "Algebra 1")
-topic = st.sidebar.text_input("Topic", "Equation and Relations")
-difficulty = st.sidebar.selectbox("Difficulty", ["beginner", "intermediate", "advanced"])
-speaking_style = st.sidebar.text_input("Speaking Style", "engaging and detailed")
-prev_lecture = st.sidebar.text_input("Previous Lecture", "Coordinate Plane")
+math_subject = st.sidebar.text_input("Math Subject", value="Algebra")
+topic = st.sidebar.text_input("Topic", value="Quadratic Equations")
+difficulty = st.sidebar.selectbox("Difficulty", options=["beginner", "intermediate", "advanced"], index=1)
+speaking_style = st.sidebar.text_input("Speaking Style", value="engaging and clear")
+example1 = st.sidebar.text_area("Example Problem", value="Solve x^2 - 5x + 6 = 0")
+problem1 = st.sidebar.text_area("Problem 1", value="Find the roots of 2x^2 - 4x - 6 = 0")
+wordproblem = st.sidebar.text_area("Word Problem", value="A projectile is launched with a height given by h(t) = -4.9t^2 + 20t + 5. Determine when it reaches the ground.")
+prev_lecture = st.sidebar.text_area("Previous Lecture Reference", value="We covered linear equations and basic factoring techniques.")
 
-# --- OCR + Text for Example Problem ---
-st.sidebar.header("Example Problem")
-uploaded_example_image = st.sidebar.file_uploader("Upload Example Problem Image (optional)", type=["png", "jpg", "jpeg"])
-example_text_input = st.sidebar.text_area(
-    "Or type Example Problem text",
-    "Given the graph with the points A(2,7), B(-1,5), C(-2,-3), and D(5,-8), determine the domain and range of the graph"
-)
+# --------------------------------------------
+# Main Area: Editable Lecture Prompt Template
+# --------------------------------------------
 
-if uploaded_example_image is not None:
-    image = Image.open(uploaded_example_image)
-    extracted_text = pytesseract.image_to_string(image)
-    st.sidebar.write("**Extracted text from Example Problem image:**", extracted_text)
-    example1 = extracted_text.strip() if extracted_text.strip() else example_text_input
-else:
-    example1 = example_text_input
-
-# --- OCR + Text for Problem 1 ---
-st.sidebar.header("Problem 1")
-uploaded_problem1_image = st.sidebar.file_uploader("Upload Problem 1 Image (optional)", type=["png", "jpg", "jpeg"])
-problem1_text_input = st.sidebar.text_area(
-    "Or type Problem 1 text",
-    "Solve the equation when the domain is {-2, -1, 0, 4, 7} for 6x - 3y = 21"
-)
+st.title("Lecture Script Generator and Refinement Interface")
 
-if uploaded_problem1_image is not None:
-    image = Image.open(uploaded_problem1_image)
-    extracted_text = pytesseract.image_to_string(image)
-    st.sidebar.write("**Extracted text from Problem 1 image:**", extracted_text)
-    problem1 = extracted_text.strip() if extracted_text.strip() else problem1_text_input
-else:
-    problem1 = problem1_text_input
-
-# --- OCR + Text for Word Problem ---
-st.sidebar.header("Word Problem")
-uploaded_wordproblem_image = st.sidebar.file_uploader("Upload Word Problem Image (optional)", type=["png", "jpg", "jpeg"])
-wordproblem_text_input = st.sidebar.text_area(
-    "Or type Word Problem text",
-    "On a Friday night, a group of friends decided to go to the movies..."
+# Build the default prompt based on current sidebar inputs.
+default_prompt = default_prompt_builder(
+    math_subject, topic, difficulty, speaking_style, example1, problem1, wordproblem, prev_lecture
 )
 
-if uploaded_wordproblem_image is not None:
-    image = Image.open(uploaded_wordproblem_image)
-    extracted_text = pytesseract.image_to_string(image)
-    st.sidebar.write("**Extracted text from Word Problem image:**", extracted_text)
-    wordproblem = extracted_text.strip() if extracted_text.strip() else wordproblem_text_input
-else:
-    wordproblem = wordproblem_text_input
-
-# --- Prompt Customization ---
-st.sidebar.header("Prompt Customization")
-edit_prompt_manually = st.sidebar.checkbox("Edit Prompt Manually?")
-
-if edit_prompt_manually:
-    # Build the default prompt for the user to see and edit
-    default_prompt = default_prompt_builder(
-        math_subject, topic, difficulty, speaking_style,
-        example1, problem1, wordproblem, prev_lecture
-    )
-    user_custom_prompt = st.sidebar.text_area(
-        "Custom Prompt (edit as you like)",
-        value=default_prompt,
-        height=600
-    )
-else:
-    user_custom_prompt = None
-
-# 1) Build the final prompt right away
-if edit_prompt_manually and user_custom_prompt:
-    final_prompt = user_custom_prompt
-else:
-    final_prompt = default_prompt_builder(
-        math_subject, topic, difficulty, speaking_style,
-        example1, problem1, wordproblem, prev_lecture
-    )
+# Initialize session state for custom prompt if not set.
+if "custom_prompt" not in st.session_state:
+    st.session_state.custom_prompt = default_prompt
 
-# 2) Display the final prompt in the main area
-st.subheader("Current Prompt Preview")
-st.text_area("Prompt Being Used - You may change this if necessary", final_prompt, height=300)
+st.subheader("Lecture Prompt Template (Editable)")
+st.write("This is the template with numbered sections and placeholders. Use the button below to update it based on the current input parameters, or edit it directly.")
+if st.button("Update Prompt Template with current placeholder values"):
+    st.session_state.custom_prompt = default_prompt
 
-# 3) Generate the script
-if st.button("Generate Lecture Script"):
-    with st.spinner("Generating script..."):
-        lecture_script = call_openai_chat(final_prompt, max_tokens=3000, temperature=0.4)
-        lecture_script = unify_all_math_as_double_dollars(lecture_script)
-    
-    st.success("Lecture script generated!")
-    st.text_area("Final Lecture Script", lecture_script, height=600)
-    st.session_state["final_script"] = lecture_script  # store for agent use
-
-# --- Agent Interaction Section ---
-st.subheader("Request Further Improvements or Checks")
-user_improvement_request = st.text_input(
-    "Request (e.g., 'Add better transitions', 'Improve step-by-step explanation', etc.)"
-)
+# Editable text area for the prompt template.
+custom_prompt = st.text_area("Edit Lecture Prompt Template", value=st.session_state.custom_prompt, height=400, key="custom_prompt_main")
+st.session_state.custom_prompt = custom_prompt
 
-if st.button("Ask the Agent"):
-    if "final_script" in st.session_state and st.session_state["final_script"]:
-        with st.spinner("Asking agent..."):
-            agent_response = ask_agent_for_improvement(st.session_state["final_script"], user_improvement_request)
-        st.text_area("Agent's Response", agent_response, height=400)
-    else:
-        st.warning("Please generate the lecture script first.")
+# --------------------------------------------
+# Lecture Script Generation, Agent Feedback, and Refinement
+# --------------------------------------------
+
+if st.button("Generate Lecture Script"):
+    with st.spinner("Generating lecture script and running agent checks..."):
+        # 1. Generate the initial lecture script.
+        lecture_script = call_openai_chat(custom_prompt)
+        # 2. Run all agent feedback functions.
+        transitions_feedback = check_transitions(lecture_script)
+        errors_feedback = check_errors(lecture_script)
+        steps_feedback = check_step_organization(lecture_script)
+        # 3. Produce the refined lecture script using the agent feedback.
+        refined_script = refine_lecture_manual(lecture_script, transitions_feedback, errors_feedback, steps_feedback)
+        # 4. Produce the scriptified lecture script by further processing the refined script with extra transitions.
+        scriptified_script = add_extra_transitions(refined_script)
+    # Save all results in session state.
+    st.session_state['lecture_script'] = lecture_script
+    st.session_state['transitions_feedback'] = transitions_feedback
+    st.session_state['errors_feedback'] = errors_feedback
+    st.session_state['steps_feedback'] = steps_feedback
+    st.session_state['refined_script'] = refined_script
+    st.session_state['scriptified_script'] = scriptified_script
+    st.success("Lecture script, agent feedback, and both refined scripts generated!")
+
+# --------------------------------------------
+# Editable Lecture Script and Update Agent Feedback
+# --------------------------------------------
+
+if "lecture_script" in st.session_state:
+    st.subheader("Generated Lecture Script (Editable)")
+    lecture_script = st.text_area("Lecture Script", value=st.session_state['lecture_script'], height=300)
+    st.session_state['lecture_script'] = lecture_script
+
+    if st.button("Update Agent Feedback"):
+        with st.spinner("Fetching updated agent feedback..."):
+            transitions_feedback = check_transitions(lecture_script)
+            errors_feedback = check_errors(lecture_script)
+            steps_feedback = check_step_organization(lecture_script)
+        st.session_state['transitions_feedback'] = transitions_feedback
+        st.session_state['errors_feedback'] = errors_feedback
+        st.session_state['steps_feedback'] = steps_feedback
+        st.success("Agent feedback updated!")
+
+# --------------------------------------------
+# Regenerate Refined Scripts if Needed
+# --------------------------------------------
+if "lecture_script" in st.session_state and st.session_state.get("transitions_feedback") is not None:
+    if st.button("Regenerate Refined Lecture Scripts"):
+        with st.spinner("Regenerating refined lecture scripts..."):
+            refined_script = refine_lecture_manual(
+                st.session_state["lecture_script"],
+                st.session_state.get('transitions_feedback', ''),
+                st.session_state.get('errors_feedback', ''),
+                st.session_state.get('steps_feedback', '')
+            )
+            scriptified_script = add_extra_transitions(refined_script)
+        st.session_state['refined_script'] = refined_script
+        st.session_state['scriptified_script'] = scriptified_script
+        st.success("Both refined lecture scripts regenerated!")
+
+# --------------------------------------------
+# Display Refined and Scriptified Lectures Side by Side
+# --------------------------------------------
+if "refined_script" in st.session_state and "scriptified_script" in st.session_state:
+    st.subheader("Refined and Scriptified Lectures")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.text_area("Refined Lecture Script", value=st.session_state['refined_script'], height=300)
+    with col2:
+        st.text_area("Scriptified Lecture Script", value=st.session_state['scriptified_script'], height=300)
+
+# --------------------------------------------
+# Display Agent Feedback Below
+# --------------------------------------------
+if "transitions_feedback" in st.session_state:
+    st.subheader("Agent Feedback")
+    st.markdown("**Transitions Feedback:**")
+    st.text_area("", value=st.session_state.get('transitions_feedback', ''), height=150)
+    st.markdown("**Errors Feedback:**")
+    st.text_area("", value=st.session_state.get('errors_feedback', ''), height=150)
+    st.markdown("**Step Organization Feedback:**")
+    st.text_area("", value=st.session_state.get('steps_feedback', ''), height=150)