Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,48 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+import os
+import langchain
+import langchain_huggingface
+import huggingface_hub
+from langchain_community.document_loaders import YoutubeLoader
+from langchain_huggingface import HuggingFaceEndpoint,ChatHuggingFace
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.getenv("hf")
+os.environ['HF_TOKEN'] = os.getenv("hf")
+meta_llm=HuggingFaceEndpoint(repo_id="meta-llama/Llama-3.1-8B-Instruct",
+                             provider="nebius",
+                             temperature=0.5,max_new_tokens=150,
+                             task="conversational")
+model=ChatHuggingFace(llm=meta_llm,
+                      repo_id="meta-llama/Llama-3.1-8B-Instruct",
+                      provider="nebius",
+                      temperature=0.5,
+                      max_new_tokens=50,
+                      task="conversational")
+def get_youtube_transcript(url):
+    loader = YoutubeLoader.from_youtube_url(url)
+    docs = loader.load()
+    return docs[0].page_content
+
+# 📝 Function to summarize
+def summarize_youtube_video(url):
+    transcript = get_youtube_transcript(url)
+    if len(transcript) > 3000:
+        transcript = transcript[:3000]  # Trim to avoid token limit
+    prompt = f"Summarize this YouTube video transcript:\n\n{transcript}"
+    response = model.invoke(prompt)
+    return response.content
+st.set_page_config(page_title="YouTube Video Summarizer", layout="centered")
+st.title("📻 YouTube Video Summarizer")
+st.write("Paste a YouTube video URL below to get a concise summary using Llama 3.1 🧠")
+
+youtube_url = st.text_input("📥 Enter YouTube URL:")
+if st.button("Summarize"):
+    if youtube_url.strip() == "":
+        st.warning("Please enter a valid YouTube video URL.")
+    else:
+        with st.spinner("Fetching and summarizing..."):
+            try:
+                summary = summarize_youtube_video(youtube_url)
+                st.success("✅ Summary Generated:")
+                st.write(summary)
+            except Exception as e:
+                st.error(f"Something went wrong: {e}")