Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,244 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+# 🧑‍🏫 AI-Powered YouTube Teaching Assistant — Enhanced Colorful UI
+
+import os
+import re
 import streamlit as st
+from youtube_transcript_api import YouTubeTranscriptApi, TranscriptsDisabled
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_together import ChatTogether, TogetherEmbeddings
+from langchain_core.prompts import PromptTemplate
+from langchain_core.runnables import RunnableParallel, RunnablePassthrough, RunnableLambda
+from langchain_core.output_parsers import StrOutputParser
+from langchain.agents import initialize_agent, Tool
+from langchain_community.tools.tavily_search import TavilySearchResults
+
+# Set API Keys
+os.environ["TOGETHER_API_KEY"] = "5c22e5f0d9af71d1cd7dfac4284fcde8260ca7db9c81a678387c74d0679da268"
+
+# LLM and Embeddings
+llm = ChatTogether(model="meta-llama/Llama-3.3-70B-Instruct-Turbo-Free", temperature=0.2)
+embeddings = TogetherEmbeddings(model="togethercomputer/m2-bert-80M-32k-retrieval")
+
+# Prompts
+note_prompt = PromptTemplate(
+    template="""
+    You're a note-taking assistant. Convert the following transcript into clear, concise lecture notes:
+    - Headings
+    - Bullet points
+    - Definitions
+    - Examples
+
+    Transcript:
+    {chunk}
+    """,
+    input_variables=["chunk"]
+)
+
+quiz_prompt = PromptTemplate(
+    template="""
+    Generate 3 multiple-choice questions from the following transcript. Include correct answers.
+
+    Transcript:
+    {chunk}
+    """,
+    input_variables=["chunk"]
+)
+
+assignment_prompt = PromptTemplate(
+    template="""
+    Based on the transcript below, generate 2 beginner-level coding exercises and short answers.
+
+    Transcript:
+    {chunk}
+    """,
+    input_variables=["chunk"]
+)
+
+compare_prompt = PromptTemplate(
+    template="""
+    Compare the following two transcripts. Highlight:
+    - Similarities
+    - Differences
+    - Unique insights
+
+    Transcript 1:
+    {transcript1}
+
+    Transcript 2:
+    {transcript2}
+    """,
+    input_variables=["transcript1", "transcript2"]
+)
+
+# Helper Functions
+def extract_video_id(url):
+    match = re.search(r"(?:v=|youtu\\.be/)([^&?]+)", url)
+    return match.group(1) if match else None
+
+def get_transcript(video_id):
+    try:
+        transcript_list = YouTubeTranscriptApi.get_transcript(video_id, languages=["en"])
+        return " ".join(chunk["text"] for chunk in transcript_list)
+    except TranscriptsDisabled:
+        return None
+
+def split_transcript(transcript):
+    splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    return splitter.create_documents([transcript])
+
+def create_vector_store(docs):
+    return FAISS.from_documents(docs, embeddings)
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+def generate_notes(chunks):
+    return [llm.invoke(note_prompt.invoke({"chunk": chunk.page_content})).content for chunk in chunks]
+
+def generate_quiz(chunks):
+    return [llm.invoke(quiz_prompt.invoke({"chunk": chunk.page_content})).content for chunk in chunks]
+
+def generate_assignments(chunks):
+    return [llm.invoke(assignment_prompt.invoke({"chunk": chunk.page_content})).content for chunk in chunks]
+
+def find_resources(query):
+    tavily = TavilySearchResults()
+    tools = [Tool.from_function(name="search", func=tavily.run, description="Web search")]
+    agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=False)
+    return agent.run(query)
+
+def compare_videos(t1, t2):
+    return llm.invoke(compare_prompt.invoke({"transcript1": t1, "transcript2": t2})).content
+
+# Streamlit UI
+st.set_page_config(page_title="🎨 AI Teaching Assistant", layout="centered")
+st.markdown("""
+    <style>
+    .main {
+        background: linear-gradient(145deg, #f0f4f8, #c3e0e5);
+        padding: 2rem;
+        border-radius: 12px;
+    }
+    .stTextInput>div>div>input {
+        border-radius: 0.75rem;
+        border: 2px solid #5dade2;
+        background-color: #fefefe;
+    }
+    .stSelectbox>div>div>div {
+        border-radius: 0.75rem;
+        background-color: #ebf5fb;
+        font-weight: bold;
+        color: #2e4053;
+    }
+    .stButton>button {
+        border-radius: 0.5rem;
+        background: linear-gradient(to right, #00c6ff, #0072ff);
+        color: white;
+        font-weight: bold;
+        padding: 0.6rem 1.2rem;
+    }
+    .block-container {
+        padding: 2rem 3rem;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+st.markdown("""
+    <h1 style='text-align: center; color: #154360;'>🎓 AI Teaching Assistant</h1>
+    <p style='text-align: center; font-size: 18px; color: #1b2631;'>Transform YouTube videos into interactive, intelligent content effortlessly!</p>
+    <hr style='border-top: 1px solid #aed6f1;'>
+""", unsafe_allow_html=True)
+
+option = st.selectbox("🎯 What do you want to do?", [
+    "Summarize",
+    "Ask a custom question",
+    "Compare with another video",
+    "Lecture Notes Generator",
+    "Quiz Generator",
+    "Assignment / Coding Problems Generator",
+    "Follow-up Resource Finder"
+])
+
+video_url = st.text_input("🔗 Enter YouTube video URL")
+video_id = extract_video_id(video_url)
+transcript = get_transcript(video_id) if video_id else None
+
+if option == "Compare with another video":
+    second_url = st.text_input("🔁 Enter second video URL to compare")
+    if st.button("🧠 Compare Videos"):
+        t1, t2 = get_transcript(extract_video_id(video_url)), get_transcript(extract_video_id(second_url))
+        if t1 and t2:
+            result = compare_videos(t1[:4000], t2[:4000])
+            st.markdown(result)
+        else:
+            st.error("One or both transcripts unavailable.")
+
+elif transcript:
+    chunks = split_transcript(transcript)
+    if option == "Summarize":
+        retriever = create_vector_store(chunks).as_retriever()
+        question = "Summarize this video"
+        chain = RunnableParallel({
+            "context": retriever | RunnableLambda(format_docs),
+            "question": RunnablePassthrough()
+        }) | PromptTemplate(
+            template="""
+            You are a helpful assistant. Use only the provided context to answer.
+
+            {context}
+            Question: {question}
+            """,
+            input_variables=["context", "question"]
+        ) | llm | StrOutputParser()
+        summary = chain.invoke(question)
+        st.text_area("📄 Summary", summary, height=300)
+
+    elif option == "Ask a custom question":
+        custom_q = st.text_input("💬 Your question about the video")
+        if st.button("🧠 Ask"):
+            retriever = create_vector_store(chunks).as_retriever()
+            chain = RunnableParallel({
+                "context": retriever | RunnableLambda(format_docs),
+                "question": RunnablePassthrough()
+            }) | PromptTemplate(
+                template="""
+                You are a helpful assistant. Use only the provided context to answer.
+
+                {context}
+                Question: {question}
+                """,
+                input_variables=["context", "question"]
+            ) | llm | StrOutputParser()
+            answer = chain.invoke(custom_q)
+            st.text_area("💡 AI Answer", answer, height=300)
+
+    elif option == "Lecture Notes Generator":
+        if st.button("📝 Generate Notes"):
+            notes = generate_notes(chunks)
+            for i, n in enumerate(notes):
+                st.markdown(f"### 📘 Section {i+1}")
+                st.markdown(n)
+
+    elif option == "Quiz Generator":
+        if st.button("🧪 Generate Quiz"):
+            quiz = generate_quiz(chunks)
+            for i, q in enumerate(quiz):
+                st.markdown(f"### ❓ Quiz {i+1}")
+                st.markdown(q)
+            st.success("✔️ Quiz Generated. (Manual review for answers)")
+
+    elif option == "Assignment / Coding Problems Generator":
+        if st.button("👨‍💻 Generate Assignments"):
+            tasks = generate_assignments(chunks)
+            for i, t in enumerate(tasks):
+                st.markdown(f"### ⚙️ Task {i+1}")
+                st.markdown(t)
+
+    elif option == "Follow-up Resource Finder":
+        if st.button("🌐 Find More Resources"):
+            followup = find_resources(f"learning resources about: {transcript[:300]}")
+            st.markdown(followup)
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+else:
+    st.warning("⚠️ Please enter a valid YouTube URL with available transcript.")