Update app.py

app.py

@@ -4,7 +4,6 @@ from groq import Groq
 from io import BytesIO
 from PyPDF2 import PdfReader
 import docx
-from docx import Document
 from textwrap import wrap
 import json
 
@@ -54,12 +53,17 @@ def chunk_text(text, max_tokens=2000):
     Returns:
         list: A list of text chunks.
     """
-    char_limit = max_tokens * 4  # Approximation: 1 token = 4 characters
+    # Approximation: 1 token = 4 characters
+    char_limit = max_tokens * 4
     chunks = wrap(text, char_limit)
     return chunks
 
 # Helper Functions
+
 def summarize_topic(text, topic):
+    """
+    Summarize a specific topic from the given text.
+    """
     messages = [
         {"role": "system", "content": "Summarize the following lesson content."},
         {"role": "user", "content": f"Context: {text}\n\nSummarize the topic: {topic}"},
@@ -67,7 +71,72 @@ def summarize_topic(text, topic):
     response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
     return response.choices[0].message.content
 
+def ask_question(text, question):
+    """
+    Answer a question based on the given text.
+    """
+    messages = [
+        {"role": "system", "content": "You are a helpful teaching assistant."},
+        {"role": "user", "content": f"Context: {text}\n\nQuestion: {question}"},
+    ]
+    response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
+    return response.choices[0].message.content
+
+def generate_mcqs(text, num_questions):
+    """
+    Generate multiple-choice questions from the given text.
+    Args:
+        text (str): The input text for generating MCQs.
+        num_questions (int): The number of MCQs to generate.
+    Returns:
+        list: A list of MCQs, or an empty list if parsing fails.
+    """
+    messages = [
+        {"role": "system", "content": "Generate multiple-choice questions for a lesson."},
+        {"role": "user", "content": f"Context: {text}\n\nGenerate {num_questions} MCQs as a JSON array. Each MCQ should have 'question', 'options', and 'answer' fields."},
+    ]
+    response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
+
+    raw_response = response.choices[0].message.content
+    st.write(f"Raw Response: {raw_response}")  # Display the raw response in Streamlit for debugging
+
+    cleaned_response = raw_response.strip()
+
+    try:
+        mcqs = json.loads(cleaned_response)
+        if isinstance(mcqs, list):
+            formatted_mcqs = []
+            for i, mcq in enumerate(mcqs, 1):
+                question = mcq['question']
+                options = "\n".join([f"{chr(97 + idx)}) {option}" for idx, option in enumerate(mcq['options'])])
+                answer = mcq['answer']
+                formatted_mcqs.append(f"**Q{i}. {question}**\n{options}\n\nAnswer: {answer}\n")
+            return formatted_mcqs
+        else:
+            st.error("The generated MCQs are not in the expected format.")
+            return []
+    except json.JSONDecodeError as e:
+        st.error(f"Error decoding MCQs: {e}. Raw response: {cleaned_response}")
+        return []
+    except Exception as e:
+        st.error(f"Unexpected error: {e}")
+        return []
+
+def adapt_lesson_for_grade(text, grade):
+    """
+    Adapt lesson content for a specific grade level.
+    """
+    messages = [
+        {"role": "system", "content": "Adapt the lesson content for a specific grade."},
+        {"role": "user", "content": f"Context: {text}\n\nAdapt this lesson for {grade}."},
+    ]
+    response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
+    return response.choices[0].message.content
+
 def generate_assignment(text, topic):
+    """
+    Generate a conceptual-based assignment from the given lesson or topic.
+    """
     messages = [
         {"role": "system", "content": "Generate a conceptual-based assignment from the following lesson content."},
         {"role": "user", "content": f"Context: {text}\n\nGenerate a conceptual-based assignment for the topic: {topic}."},
@@ -75,33 +144,23 @@ def generate_assignment(text, topic):
     response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
     return response.choices[0].message.content
 
-# Function to export content to a Word document
-def export_to_docx(content, filename):
+def provide_learning_resources(topic):
     """
-    Exports the provided content to a .docx file.
-    Args:
-        content (str): The text content to be exported.
-        filename (str): The desired filename for the exported document.
+    Generate a list of learning resources for a specific topic.
     """
-    doc = Document()
-    doc.add_heading("Exported Content", level=1)
-    doc.add_paragraph(content)
-    buffer = BytesIO()
-    doc.save(buffer)
-    buffer.seek(0)
-    st.download_button(
-        label=f"Download {filename}",
-        data=buffer,
-        file_name=filename,
-        mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-    )
+    messages = [
+        {"role": "system", "content": "Provide a list of learning resources for a specific topic."},
+        {"role": "user", "content": f"Generate a list of books, websites, or courses for learning about: {topic}."},
+    ]
+    response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
+    return response.choices[0].message.content
 
 # Streamlit app layout
 st.title("EduAI Assistant for Teachers")
 st.markdown("""
 Welcome to the AI-powered teaching assistant!  
 - Upload your lesson files or input text.  
-- Ask questions, summarize topics, create quizzes and assignments tailored to different grades.
+- Ask questions, summarize topics, create quizzes, assignments, and access learning resources.
 """)
 
 # Sidebar: File Upload and Options
@@ -113,12 +172,15 @@ manual_input = st.sidebar.text_area("Or paste lesson text here", height=200)
 st.sidebar.header("Select Action")
 task = st.sidebar.selectbox("What would you like to do?", [
     "Summarize a Topic",
-    "Generate Conceptual Assignment"
+    "Ask Questions",
+    "Generate MCQs",
+    "Adapt Lesson for Grades",
+    "Generate Conceptual Assignment",
+    "Provide Learning Resources"  # New feature added
 ])
 
 # Main Actions
 if manual_input or uploaded_files:
-    # Combine uploaded files into a single text
     combined_text = ""
     if uploaded_files:
         for file in uploaded_files:
@@ -132,25 +194,55 @@ if manual_input or uploaded_files:
                 st.stop()
 
     lesson_text = combined_text if uploaded_files else manual_input
-    text_chunks = chunk_text(lesson_text)  # Split text into manageable chunks
+    text_chunks = chunk_text(lesson_text)
 
     if task == "Summarize a Topic":
         topic = st.text_input("Enter the topic or keywords:")
         if st.button("Summarize"):
             summaries = [summarize_topic(chunk, topic) for chunk in text_chunks]
-            final_summary = "\n\n".join(summaries)
             st.write("### Summary")
-            st.write(final_summary)
-            export_to_docx(final_summary, "Summary.docx")
+            st.write("\n\n".join(summaries))
+
+    elif task == "Ask Questions":
+        question = st.text_input("Enter your question:")
+        if st.button("Get Answer"):
+            answers = [ask_question(chunk, question) for chunk in text_chunks]
+            st.write("### Answer")
+            st.write("\n\n".join(answers))
+
+    elif task == "Generate MCQs":
+        num_questions = st.slider("Number of questions to generate:", 1, 10, 5)
+        if st.button("Generate MCQs"):
+            mcqs = []
+            for chunk in text_chunks:
+                mcqs.extend(generate_mcqs(chunk, num_questions))
+            st.write("### Multiple Choice Questions")
+            for i, mcq in enumerate(mcqs, 1):
+                st.write(f"**Q{i}. {mcq['question']}**")
+                for option in mcq['options']:
+                    st.write(f"- {option}")
+                st.write(f"**Answer:** {mcq['answer']}")
+                st.write("---")
+
+    elif task == "Adapt Lesson for Grades":
+        grade = st.selectbox("Select the target grade level:", [f"Grade {i}" for i in range(1, 17)])
+        if st.button("Adapt Lesson"):
+            adapted_lessons = [adapt_lesson_for_grade(chunk, grade) for chunk in text_chunks]
+            st.write(f"### Lesson Adapted for {grade}")
+            st.write("\n\n".join(adapted_lessons))
 
     elif task == "Generate Conceptual Assignment":
         topic = st.text_input("Enter the topic for assignment generation:")
         if st.button("Generate Assignment"):
             assignments = [generate_assignment(chunk, topic) for chunk in text_chunks]
-            final_assignment = "\n\n".join(assignments)
             st.write("### Conceptual Assignment")
-            st.write(final_assignment)
-            export_to_docx(final_assignment, "Assignment.docx")
-
+            st.write("\n\n".join(assignments))
+
+    elif task == "Provide Learning Resources":
+        topic = st.text_input("Enter the topic for learning resources:")
+        if st.button("Generate Resources"):
+            resources = provide_learning_resources(topic)
+            st.write("### Learning Resources")
+            st.write(resources)
 else:
     st.info("Please upload files or enter lesson text to begin.")