Create app.py

app.py

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+import os
+import streamlit as st
+from groq import Groq
+from io import BytesIO
+from PyPDF2 import PdfReader
+import docx
+
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("Groq_Api_Key"))
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_file):
+    pdf_reader = PdfReader(pdf_file)
+    text = ""
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+    return text
+
+# Function to extract text from Word document
+def extract_text_from_word(doc_file):
+    doc = docx.Document(doc_file)
+    text = "\n".join([paragraph.text for paragraph in doc.paragraphs])
+    return text
+
+# Helper Functions
+
+def summarize_topic(text, topic):
+    messages = [
+        {"role": "system", "content": "Summarize the following lesson content."},
+        {"role": "user", "content": f"Context: {text}\n\nSummarize the topic: {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):
+    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):
+    messages = [
+        {"role": "system", "content": "Generate multiple-choice questions for a lesson."},
+        {"role": "user", "content": f"Context: {text}\n\nGenerate {num_questions} MCQs."},
+    ]
+    response = client.chat.completions.create(messages=messages, model="llama-3.3-70b-versatile", stream=False)
+    return eval(response.choices[0].message.content)
+
+def adapt_lesson_for_grade(text, grade):
+    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
+
+# Streamlit app layout
+st.title("AI Assistant for Teachers")
+st.markdown("""
+Welcome to the AI-powered teaching assistant!  
+- Upload your lesson files or input text.  
+- Ask questions, summarize topics, or create quizzes and assignments tailored to different grades.
+""")
+
+# Sidebar: File Upload and Options
+st.sidebar.header("Upload Files or Enter Text")
+uploaded_files = st.sidebar.file_uploader("Upload lesson files (PDFs or Word documents)", accept_multiple_files=True)
+manual_input = st.sidebar.text_area("Or paste lesson text here", height=200)
+
+# Select Functionality
+st.sidebar.header("Select Action")
+task = st.sidebar.selectbox("What would you like to do?", [
+    "Summarize a Topic",
+    "Ask Questions",
+    "Generate MCQs",
+    "Adapt Lesson for Grades"
+])
+
+# 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:
+            file_type = file.name.split(".")[-1].lower()
+            if file_type == "pdf":
+                combined_text += extract_text_from_pdf(file)
+            elif file_type == "docx":
+                combined_text += extract_text_from_word(file)
+            else:
+                st.error(f"Unsupported file type: {file_type}")
+                st.stop()
+
+    lesson_text = combined_text if uploaded_files else manual_input
+
+    if task == "Summarize a Topic":
+        topic = st.text_input("Enter the topic or keywords:")
+        if st.button("Summarize"):
+            summary = summarize_topic(lesson_text, topic)
+            st.write("### Summary")
+            st.write(summary)
+
+    elif task == "Ask Questions":
+        question = st.text_input("Enter your question:")
+        if st.button("Get Answer"):
+            answer = ask_question(lesson_text, question)
+            st.write("### Answer")
+            st.write(answer)
+
+    elif task == "Generate MCQs":
+        num_questions = st.slider("Number of questions to generate:", 1, 10, 5)
+        if st.button("Generate MCQs"):
+            mcqs = generate_mcqs(lesson_text, 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:", ["Grade 1", "Grade 5", "Grade 8", "Grade 12"])
+        if st.button("Adapt Lesson"):
+            adapted_lesson = adapt_lesson_for_grade(lesson_text, grade)
+            st.write(f"### Lesson Adapted for {grade}")
+            st.write(adapted_lesson)
+
+else:
+    st.info("Please upload files or enter lesson text to begin.")