Upload app.py

app.py

@@ -0,0 +1,207 @@
+import requests
+import json
+import os
+import xml.etree.ElementTree as ET
+import openai
+import edge_tts
+from pydub import AudioSegment
+import re
+import time
+import asyncio
+import streamlit as st
+from datetime import datetime
+from dotenv import load_dotenv
+
+load_dotenv()
+
+PODCAST_FILE = "merged_audio.mp3"
+SHOW_NOTES_FILE = "show_notes.txt"
+CONVERSATION_FILE = "conversation.json"
+LAST_RUN_FILE = "last_run.txt"
+
+feed_url = "http://papers.takara.ai/api/feed"
+response = requests.get(feed_url)
+tree = ET.ElementTree(ET.fromstring(response.text))
+
+daily_feed = ""
+items = []
+for item in tree.iter("item"):
+    title = item.find("title").text
+    link = item.find("link").text
+    description = item.find("description").text
+    items.append({"title": title, "link": link, "description": description})
+    daily_feed += f"Title: {title.strip()}\nDescription: {description}\n\n"
+
+# OpenAI API client
+client = openai.Client(
+    api_key=os.getenv("DEEPINFRA_API"),
+    base_url="https://api.deepinfra.com/v1/openai",
+)
+
+
+def build_prompt(text, items):
+    """Generates a concise podcast conversation covering all research papers, ensuring correct JSON format."""
+    paper_summaries = "\n".join(
+        [f"- {item['title']} ({item['link']}): {item['description']}" for item in items]
+    )
+
+    # JSON Template for correct formatting
+    template = """
+    {
+        "conversation": [
+            {"speaker": "Brian", "text": ""},
+            {"speaker": "Jenny", "text": ""}
+        ]
+    }
+    """
+
+    return (
+        f"🎙️ Welcome to Daily Papers! Today, we're diving into the latest AI research in a quick, engaging, and "
+        f"informative discussion. This podcast should be **under 5 minutes**, so keep it concise while still covering "
+        f"the key points of each paper.\n\n"
+        f"Here are today's research papers:\n{paper_summaries}\n\n"
+        f"Convert this into a **short, engaging** podcast conversation between two experts, Brian and Jenny. "
+        f"Use a **natural tone, casual phrasing, and occasional filler words like 'uhm' and 'you know'** to keep it realistic. "
+        f"Summarize the key insights **briefly** without excessive details. Prioritize a fast-paced but informative flow. "
+        f"Ensure all papers are **mentioned**, but do not spend much time on each.\n\n"
+        f"Please follow this exact JSON format:\n{template}"
+    )
+
+
+
+def extract_conversation(text, items, max_retries=3):
+    """Extracts podcast conversation from OpenAI API with retries."""
+    for attempt in range(1, max_retries + 1):
+        try:
+            print(f"Attempt {attempt} to generate conversation...")
+            chat_completion = client.chat.completions.create(
+                messages=[{"role": "user", "content": build_prompt(text, items)}],
+                model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
+                temperature=0.7,
+                max_tokens=4096,
+            )
+            pattern = r"\{(?:[^{}]|(?:\{[^{}]*\}))*\}"
+            json_match = re.search(pattern, chat_completion.choices[0].message.content)
+            if json_match:
+                return json.loads(json_match.group())
+            raise ValueError("No valid JSON found in response")
+        except Exception as e:
+            print(f"Error: {e}")
+            if attempt < max_retries:
+                time.sleep(2)
+            else:
+                raise RuntimeError(f"Failed after {max_retries} attempts.")
+
+def generate_show_notes(items):
+    """Creates structured show notes summarizing research papers."""
+    notes = "**Show Notes**\n\nIn today's episode:\n\n"
+    for i, item in enumerate(items):
+        notes += f"{i+1}. **{item['title']}**\n   - {item['description']}\n   - [Read More]({item['link']})\n\n"
+    return notes
+
+async def generate_audio_parallel(conversation):
+    """Generates audio for the podcast in parallel."""
+    tasks = []
+    audio_files = []
+
+    for i, item in enumerate(conversation["conversation"]):
+        text = item["text"]
+        output_file = f"audio_{i}.mp3"
+        voice = "en-US-AndrewMultilingualNeural" if item["speaker"] == "Brian" else "en-US-AvaMultilingualNeural"
+        
+        # Create async TTS tasks
+        tasks.append(edge_tts.Communicate(text=text, voice=voice).save(output_file))
+        audio_files.append(output_file)
+
+    # Run all tasks concurrently
+    await asyncio.gather(*tasks)
+    return audio_files
+
+def merge_audio_files(audio_files, output_file="merged_audio.mp3"):
+    """Merges multiple audio files into one MP3 file."""
+    combined = AudioSegment.empty()
+    for file in audio_files:
+        audio = AudioSegment.from_file(file)
+        combined += audio
+    combined.export(output_file, format="mp3")
+    print(f"Merged audio saved to {output_file}")
+
+def save_to_file(content, filename):
+    """Saves content to a file."""
+    with open(filename, "w") as file:
+        file.write(content)
+
+def load_conversation():
+    """Loads conversation from file if exists."""
+    if os.path.exists(CONVERSATION_FILE):
+        with open(CONVERSATION_FILE, "r", encoding="utf-8") as file:
+            return json.load(file)
+    return None
+
+async def generate_podcast():
+    """Generates podcast content (once per day or on force generate)."""
+    if os.path.exists(LAST_RUN_FILE):
+        with open(LAST_RUN_FILE, "r") as file:
+            last_run_date = file.read().strip()
+        if last_run_date == datetime.today().strftime("%Y-%m-%d"):
+            print("Podcast already generated today. Skipping...")
+            return
+
+    conversation = extract_conversation(daily_feed, items)
+    save_to_file(json.dumps(conversation, indent=2), CONVERSATION_FILE)
+    save_to_file(generate_show_notes(items), SHOW_NOTES_FILE)
+
+    print("Generating audio...")
+    audio_files = await generate_audio_parallel(conversation)
+    merge_audio_files(audio_files)
+
+    for file in audio_files:
+        os.remove(file)
+
+    with open(LAST_RUN_FILE, "w") as file:
+        file.write(datetime.today().strftime("%Y-%m-%d"))
+
+    print("Podcast and show notes generated successfully.")
+
+# Generate daily podcast
+asyncio.run(generate_podcast())
+
+# Streamlit UI
+st.set_page_config(page_title="AI Podcast", layout="wide")
+
+conversation_data = load_conversation()
+show_notes = "**No show notes available.**"
+if os.path.exists(SHOW_NOTES_FILE):
+    with open(SHOW_NOTES_FILE, "r", encoding="utf-8") as f:
+        show_notes = f.read()
+
+st.title("🎙️ Today's AI Podcast")
+
+col1, col2 = st.columns([2, 1])
+
+with col1:
+    st.subheader("🎧 Listen to the Podcast")
+    if os.path.exists(PODCAST_FILE):
+        audio_bytes = open(PODCAST_FILE, "rb").read()
+        st.audio(audio_bytes, format="audio/mp3")
+    else:
+        st.warning("No podcast available. Please generate an episode.")
+
+    st.subheader("📜 Show Notes")
+    st.markdown(show_notes)
+
+with col2:
+    st.subheader("🗨️ Podcast Conversation")
+    if conversation_data:
+        for msg in conversation_data["conversation"]:
+            st.write(f"**{msg['speaker']}**: {msg['text']}")
+    else:
+        st.warning("No conversation data available.")
+
+st.markdown("---")
+
+if st.button("🔄 Force Generate Podcast"):
+    asyncio.run(generate_podcast())
+    st.rerun()
+
+st.markdown("📢 **Stay tuned for more AI research insights!**")