Update app.py

app.py

@@ -6,6 +6,8 @@ import os
 import numpy as np
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
+import speech_recognition as sr
+from io import StringIO
 
 # Function to extract text from a PDF file
 def extract_text_from_pdf(pdf_file):
@@ -39,6 +41,15 @@ def save_code_to_file(code, filename="generated_code.txt"):
     with open(filename, "w") as f:
         f.write(code)
 
+# Function to generate AI-based study notes and summaries
+def generate_summary(text):
+    prompt = f"Summarize the following text into key points:\n\n{text}"
+    response = openai.ChatCompletion.create(
+        model="gpt-4o-mini",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return response['choices'][0]['message']['content']
+
 # Streamlit app starts here
 st.title("AI Assistance")
 
@@ -50,7 +61,7 @@ if openai_api_key:
 
     # Sidebar to toggle between Course Query Assistant and Code Generator
     st.sidebar.title("Select Mode")
-    mode = st.sidebar.radio("Choose an option", ("Course Query Assistant", "Code Generator"))
+    mode = st.sidebar.radio("Choose an option", ("Course Query Assistant", "Code Generator", "AI Chatbot Tutor", "AI Study Notes & Summaries"))
 
     if mode == "Course Query Assistant":
         st.header("Course Query Assistant")
@@ -147,3 +158,47 @@ if openai_api_key:
                     )
             else:
                 st.error("Please provide a prompt to generate the code.")
+
+    elif mode == "AI Chatbot Tutor":
+        st.header("AI Chatbot Tutor")
+
+        # Chat interface for the AI tutor
+        chat_history = []
+
+        def chat_with_bot(query):
+            chat_history.append({"role": "user", "content": query})
+            response = openai.ChatCompletion.create(
+                model="gpt-4o-mini",
+                messages=chat_history
+            )
+            chat_history.append({"role": "assistant", "content": response['choices'][0]['message']['content']})
+            return response['choices'][0]['message']['content']
+
+        user_query = st.text_input("Ask a question:")
+
+        if user_query:
+            with st.spinner("Getting answer..."):
+                bot_response = chat_with_bot(user_query)
+                st.write(f"### AI Response: {bot_response}")
+
+    elif mode == "AI Study Notes & Summaries":
+        st.header("AI Study Notes & Summaries")
+
+        # Upload course materials for summarization
+        uploaded_files_for_summary = st.file_uploader("Upload Course Materials (PDFs) for Summarization", type=["pdf"], accept_multiple_files=True)
+
+        if uploaded_files_for_summary:
+            st.write("Generating study notes and summaries...")
+
+            # Extract text from PDFs
+            all_text = ""
+            for uploaded_file in uploaded_files_for_summary:
+                text = extract_text_from_pdf(uploaded_file)
+                all_text += text
+
+            # Generate summary using AI
+            summary = generate_summary(all_text)
+
+            # Display the summary
+            st.write("### AI-Generated Summary:")
+            st.write(summary)