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import streamlit as st
import os
import tempfile
import gc
import base64
import time
import yaml
from tqdm import tqdm
from datetime import datetime
from typing import Optional
from crawl4ai_scrapper import scrape_multiple_channels
from crewai import Agent, Crew, Process, Task, LLM
from crewai_tools import FileReadTool
from dotenv import load_dotenv
load_dotenv()
# ===========================
# Cerebras LLM Integration
# ===========================
class CerebrasLLM(LLM):
def __init__(self, model: str, api_key: str, base_url: str, **kwargs):
from llama_index.llms.cerebras import Cerebras
self.client = Cerebras(
model=model,
api_key=api_key,
base_url=base_url,
**kwargs
)
def generate(self, prompt: str, **kwargs) -> str:
response = self.client.complete(prompt, **kwargs)
return response.text
@st.cache_resource
def load_llm() -> CerebrasLLM:
return CerebrasLLM(
model="llama-3.3-70b",
api_key=os.getenv("CEREBRAS_API_KEY"),
base_url="https://api.cerebras.ai/v1",
temperature=0.7,
max_tokens=4096,
top_p=0.95,
timeout=30
)
# ===========================
# Core Application Logic
# ===========================
class YouTubeAnalyzer:
def __init__(self):
self.docs_tool = FileReadTool()
self.llm = load_llm()
def create_crew(self):
with open("config.yaml", 'r') as file:
config = yaml.safe_load(file)
analysis_agent = Agent(
role=config["agents"][0]["role"],
goal=config["agents"][0]["goal"],
backstory=config["agents"][0]["backstory"],
verbose=True,
tools=[self.docs_tool],
llm=self.llm,
memory=True
)
synthesis_agent = Agent(
role=config["agents"][1]["role"],
goal=config["agents"][1]["goal"],
backstory=config["agents"][1]["backstory"],
verbose=True,
llm=self.llm,
allow_delegation=False
)
analysis_task = Task(
description=config["tasks"][0]["description"],
expected_output=config["tasks"][0]["expected_output"],
agent=analysis_agent,
output_file="analysis_raw.md"
)
synthesis_task = Task(
description=config["tasks"][1]["description"],
expected_output=config["tasks"][1]["expected_output"],
agent=synthesis_agent,
context=[analysis_task],
output_file="final_report.md"
)
return Crew(
agents=[analysis_agent, synthesis_agent],
tasks=[analysis_task, synthesis_task],
process=Process.sequential,
verbose=2
)
# ===========================
# Streamlit Interface
# ===========================
class StreamlitApp:
def __init__(self):
self.analyzer = YouTubeAnalyzer()
self._init_session_state()
def _init_session_state(self):
if "response" not in st.session_state:
st.session_state.response = None
if "crew" not in st.session_state:
st.session_state.crew = None
if "youtube_channels" not in st.session_state:
st.session_state.youtube_channels = [""]
def _setup_sidebar(self):
with st.sidebar:
st.header("YouTube Analysis Configuration")
# Channel Management
for i, channel in enumerate(st.session_state.youtube_channels):
cols = st.columns([6, 1])
with cols[0]:
url = st.text_input(
"Channel URL",
value=channel,
key=f"channel_{i}",
help="Example: https://www.youtube.com/@ChannelName"
)
with cols[1]:
if i > 0 and st.button("❌", key=f"remove_{i}"):
st.session_state.youtube_channels.pop(i)
st.rerun()
st.button("Add Channel ➕", on_click=lambda: st.session_state.youtube_channels.append(""))
# Date Selection
st.divider()
st.subheader("Analysis Period")
self.start_date = st.date_input("Start Date", key="start_date")
self.end_date = st.date_input("End Date", key="end_date")
# Analysis Control
st.divider()
if st.button("🚀 Start Analysis", type="primary"):
self._trigger_analysis()
def _trigger_analysis(self):
with st.spinner('Initializing deep content analysis...'):
try:
valid_urls = [
url for url in st.session_state.youtube_channels
if self._is_valid_youtube_url(url)
]
if not valid_urls:
st.error("Please provide at least one valid YouTube channel URL")
return
# Scrape and process data
channel_data = asyncio.run(
scrape_multiple_channels(
valid_urls,
start_date=self.start_date.strftime("%Y-%m-%d"),
end_date=self.end_date.strftime("%Y-%m-%d")
)
)
# Save transcripts
self._save_transcripts(channel_data)
# Execute analysis
with st.spinner('Running AI-powered analysis...'):
st.session_state.crew = self.analyzer.create_crew()
st.session_state.response = st.session_state.crew.kickoff(
inputs={"files": st.session_state.all_files}
)
except Exception as e:
st.error(f"Analysis failed: {str(e)}")
st.stop()
def _save_transcripts(self, channel_data):
st.session_state.all_files = []
os.makedirs("transcripts", exist_ok=True)
with tqdm(total=sum(len(ch) for ch in channel_data), desc="Processing Videos") as pbar:
for channel in channel_data:
for video in channel:
file_path = f"transcripts/{video['id']}.txt"
with open(file_path, "w") as f:
f.write("\n".join(
[f"[{seg['start']}-{seg['end']}] {seg['text']}"
for seg in video['transcript']]
))
st.session_state.all_files.append(file_path)
pbar.update(1)
def _display_results(self):
st.markdown("## Analysis Report")
with st.expander("View Full Technical Analysis"):
st.markdown(st.session_state.response)
col1, col2 = st.columns([3, 1])
with col1:
st.download_button(
label="📥 Download Full Report",
data=st.session_state.response,
file_name="youtube_analysis_report.md",
mime="text/markdown"
)
with col2:
if st.button("🔄 New Analysis"):
gc.collect()
st.session_state.response = None
st.rerun()
@staticmethod
def _is_valid_youtube_url(url: str) -> bool:
return any(pattern in url for pattern in ["youtube.com/", "youtu.be/"])
def run(self):
# Move st.set_page_config to the top to fix the error
st.set_page_config(page_title="YouTube Intelligence System", layout="wide") # First Streamlit command
st.title("YouTube Content Analysis Platform")
st.markdown("---")
self._setup_sidebar()
if st.session_state.response:
self._display_results()
else:
st.info("Configure analysis parameters in the sidebar to begin") |