mirror of
https://github.com/Shubhamsaboo/awesome-llm-apps.git
synced 2026-05-02 01:37:20 -05:00
streamlit inclusion
This commit is contained in:
Madhu
@@ -21,10 +21,129 @@ import asyncio
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import json
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from datetime import datetime
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import time
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import streamlit as st
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load_dotenv()
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def init_session_state():
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"""Initialize session state variables for storing API keys and configurations."""
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defaults = {
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"initialized": False,
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"qdrant_url": "",
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"qdrant_api_key": "",
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"firecrawl_api_key": "",
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"openai_api_key": "",
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"doc_url": "",
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"setup_complete": False,
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"client": None,
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"embedding_model": None,
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"processor_agent": None,
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"tts_agent": None,
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"selected_voice": "coral" # Default voice
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}
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for key, value in defaults.items():
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if key not in st.session_state:
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st.session_state[key] = value
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def sidebar_config():
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"""Render and handle the configuration sidebar."""
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with st.sidebar:
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st.title("🔑 Configuration")
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st.markdown("---")
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# API Keys and URLs
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st.session_state.qdrant_url = st.text_input(
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"Qdrant URL",
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value=st.session_state.qdrant_url,
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type="password"
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)
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st.session_state.qdrant_api_key = st.text_input(
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"Qdrant API Key",
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value=st.session_state.qdrant_api_key,
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type="password"
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)
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st.session_state.firecrawl_api_key = st.text_input(
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"Firecrawl API Key",
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value=st.session_state.firecrawl_api_key,
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type="password"
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)
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st.session_state.openai_api_key = st.text_input(
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"OpenAI API Key",
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value=st.session_state.openai_api_key,
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type="password"
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)
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st.markdown("---")
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st.session_state.doc_url = st.text_input(
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"Documentation URL",
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value=st.session_state.doc_url,
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placeholder="https://docs.example.com"
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)
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# Voice selection
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st.markdown("---")
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st.markdown("### 🎤 Voice Settings")
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voices = ["alloy", "ash", "ballad", "coral", "echo", "fable", "onyx", "nova", "sage", "shimmer", "verse"]
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st.session_state.selected_voice = st.selectbox(
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"Select Voice",
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options=voices,
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index=voices.index(st.session_state.selected_voice),
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help="Choose the voice for the audio response"
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)
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# Setup button
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if st.button("Initialize System", type="primary"):
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if all([
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st.session_state.qdrant_url,
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st.session_state.qdrant_api_key,
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st.session_state.firecrawl_api_key,
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st.session_state.openai_api_key,
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st.session_state.doc_url
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]):
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progress_placeholder = st.empty()
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with progress_placeholder.container():
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try:
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# Setup Qdrant
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st.markdown("🔄 Setting up Qdrant connection...")
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client, embedding_model = setup_qdrant_collection(
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st.session_state.qdrant_url,
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st.session_state.qdrant_api_key
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)
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st.session_state.client = client
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st.session_state.embedding_model = embedding_model
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st.markdown("✅ Qdrant setup complete!")
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# Crawl documentation
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st.markdown("🔄 Crawling documentation pages...")
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pages = crawl_documentation(
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st.session_state.firecrawl_api_key,
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st.session_state.doc_url
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)
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st.markdown(f"✅ Crawled {len(pages)} documentation pages!")
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# Store embeddings
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store_embeddings(
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client,
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embedding_model,
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pages,
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"docs_embeddings"
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)
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# Setup agents
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processor_agent, tts_agent = setup_agents(
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st.session_state.openai_api_key
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)
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st.session_state.processor_agent = processor_agent
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st.session_state.tts_agent = tts_agent
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st.session_state.setup_complete = True
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st.success("✅ System initialized successfully!")
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except Exception as e:
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st.error(f"Error during setup: {str(e)}")
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else:
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st.error("Please fill in all the required fields!")
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def setup_qdrant_collection(qdrant_url: str, qdrant_api_key: str, collection_name: str = "docs_embeddings"):
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print("\n--- Step 1: Setting up Qdrant Collection ---")
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@@ -194,167 +313,157 @@ async def process_query(
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collection_name: str,
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openai_api_key: str
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):
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print("\n--- Step 5: Processing Query ---")
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try:
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# Generate query embedding
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print("Generating query embedding...")
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query_embedding = list(embedding_model.embed([query]))[0]
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print(f"✓ Generated query embedding with shape: {len(query_embedding)}")
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print(f"Vector sample (first 5 elements): {query_embedding[:5]}")
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# Try to get collection info first
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print("\nVerifying collection status...")
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try:
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collection_info = client.get_collection(collection_name)
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print(f"Collection exists with {collection_info.points_count} points")
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except Exception as e:
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print(f"Warning: Could not get collection info: {str(e)}")
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# Search in Qdrant
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search_response = client.query_points(
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collection_name=collection_name,
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query=query_embedding.tolist(),
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limit=3,
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with_payload=True
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)
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# Attempt search with query parameter (confirmed working)
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print("\nAttempting vector search...")
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try:
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print("Querying with 'query' parameter...")
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search_response = client.query_points(
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collection_name=collection_name,
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query=query_embedding.tolist(),
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limit=3,
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with_payload=True
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)
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print("✓ Query successful")
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# Debug search response
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print("\nSearch Response Debug:")
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print(f"Response type: {type(search_response)}")
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# Get points from the response
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if hasattr(search_response, 'points'):
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search_results = search_response.points
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else:
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search_results = []
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search_results = search_response.points if hasattr(search_response, 'points') else []
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if not search_results:
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raise Exception("No relevant documents found in the vector database")
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# Build context from search results
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context = "Based on the following documentation:\n\n"
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for result in search_results:
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payload = result.payload
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if not payload:
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continue
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url = payload.get('url', 'Unknown URL')
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content = payload.get('content', '')
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context += f"From {url}:\n{content}\n\n"
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context += f"\nUser Question: {query}\n\n"
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context += "Please provide a clear, concise answer that can be easily spoken out loud."
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# Process response with agents
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processor_result = await Runner.run(processor_agent, context)
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processor_response = processor_result.final_output
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tts_result = await Runner.run(tts_agent, processor_response)
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tts_response = tts_result.final_output
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# Generate audio
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async_openai = AsyncOpenAI(api_key=openai_api_key)
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audio_response = await async_openai.audio.speech.create(
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model="gpt-4o-mini-tts",
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voice=st.session_state.selected_voice,
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input=processor_response,
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instructions=tts_response,
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response_format="mp3"
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)
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# Save audio to a temporary file
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temp_dir = tempfile.gettempdir()
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audio_path = os.path.join(temp_dir, f"response_{uuid.uuid4()}.mp3")
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# Write the audio content to the file
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with open(audio_path, "wb") as f:
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f.write(audio_response.content)
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print(f"\n✓ Found {len(search_results)} relevant documents")
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if not search_results:
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raise Exception("No relevant documents found in the vector database")
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# Build context from search results
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context = "Based on the following documentation:\n\n"
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for result in search_results:
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payload = result.payload
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if not payload:
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print(f"Warning: Result missing payload")
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continue
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url = payload.get('url', 'Unknown URL')
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content = payload.get('content', '')
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score = getattr(result, 'score', 'N/A')
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print(f"\nDocument from {url}")
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print(f"Relevance score: {score}")
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context += f"From {url}:\n{content}\n\n"
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context += f"\nUser Question: {query}\n\n"
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context += "Please provide a clear, concise answer that can be easily spoken out loud."
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# Process response with agents
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print("\nProcessing with Documentation Agent...")
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processor_result = await Runner.run(processor_agent, context)
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processor_response = processor_result.final_output
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print("✓ Generated text response")
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print("\nProcessing with TTS Agent...")
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tts_result = await Runner.run(tts_agent, processor_response)
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tts_response = tts_result.final_output
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print("✓ Generated TTS instructions")
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# Generate and play audio
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print("\nGenerating audio response...")
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async_openai = AsyncOpenAI(api_key=openai_api_key)
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async with async_openai.audio.speech.with_streaming_response.create(
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model="tts-1",
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voice="alloy",
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input=processor_response,
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instructions=tts_response,
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response_format="pcm"
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) as response:
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print("✓ Streaming audio response")
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await LocalAudioPlayer().play(response)
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return {
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"status": "success",
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"text_response": processor_response,
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"tts_instructions": tts_response,
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"sources": [r.payload.get("url", "Unknown URL") for r in search_results if r.payload],
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"query_details": {
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"vector_size": len(query_embedding),
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"results_found": len(search_results),
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"collection_name": collection_name
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}
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return {
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"status": "success",
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"text_response": processor_response,
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"tts_instructions": tts_response,
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"audio_path": audio_path,
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"sources": [r.payload.get("url", "Unknown URL") for r in search_results if r.payload],
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"query_details": {
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"vector_size": len(query_embedding),
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"results_found": len(search_results),
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"collection_name": collection_name
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}
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except Exception as e:
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print(f"Error during vector search: {str(e)}")
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print("Full error details:")
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import traceback
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traceback.print_exc()
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raise
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}
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except Exception as e:
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print(f"\nError processing query: {str(e)}")
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print("Full error details:")
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import traceback
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traceback.print_exc()
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return {
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"status": "error",
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"error": str(e),
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"error_details": traceback.format_exc(),
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"query": query
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}
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async def main():
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try:
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env_vars = get_env_vars()
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print("✓ Loaded environment variables")
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client, embedding_model = setup_qdrant_collection(
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env_vars["QDRANT_URL"],
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env_vars["QDRANT_API_KEY"]
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)
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pages = crawl_documentation(
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env_vars["FIRECRAWL_API_KEY"],
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"https://docs.agentmail.to/api-reference",
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"crawled_docs"
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)
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store_embeddings(client, embedding_model, pages, "docs_embeddings")
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processor_agent, tts_agent = setup_agents(env_vars["OPENAI_API_KEY"])
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query = "What are the required parameters for List Threads API of Agent Mail?"
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result = await process_query(
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query,
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client,
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embedding_model,
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processor_agent,
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tts_agent,
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"docs_embeddings",
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env_vars["OPENAI_API_KEY"]
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)
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print("\n--- Final Results ---")
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print(json.dumps(result, indent=2))
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except ValueError as e:
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print(f"\nConfiguration Error: {str(e)}")
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print("\nPlease ensure your .env file contains all required variables:")
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print("FIRECRAWL_API_KEY=your_key")
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print("QDRANT_URL=your_qdrant_url")
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print("QDRANT_API_KEY=your_qdrant_key")
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print("OPENAI_API_KEY=your_openai_key")
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except Exception as e:
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print(f"\nError: {str(e)}")
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def run_streamlit():
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"""Main Streamlit application."""
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st.set_page_config(
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page_title="AI Voice Documentation Agent Team",
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page_icon="🎙️",
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layout="wide"
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)
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init_session_state()
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sidebar_config()
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# Main content area
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st.title("🎙️ AI Voice Documentation Agent Team")
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st.markdown("""
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Get OpenAI SDK voice-powered answers to your documentation questions! Simply:
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1. Configure your API keys in the sidebar
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2. Enter the documentation URL you want to learn about or have questions about
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3. Ask your question below and get both text and voice responses
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""")
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# Query input and processing
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query = st.text_input(
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"What would you like to know about the documentation?",
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placeholder="e.g., How do I authenticate API requests?",
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disabled=not st.session_state.setup_complete
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)
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if query and st.session_state.setup_complete:
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with st.status("Processing your query...", expanded=True) as status:
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try:
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st.markdown("🔄 Searching documentation and generating response...")
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result = asyncio.run(process_query(
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query,
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st.session_state.client,
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st.session_state.embedding_model,
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st.session_state.processor_agent,
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st.session_state.tts_agent,
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"docs_embeddings",
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st.session_state.openai_api_key
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))
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if result["status"] == "success":
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status.update(label="✅ Query processed!", state="complete")
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st.markdown("### Response:")
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st.write(result["text_response"])
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if "audio_path" in result:
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st.markdown(f"### 🔊 Audio Response (Voice: {st.session_state.selected_voice})")
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# Pass the file path directly to st.audio
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st.audio(result["audio_path"], format="audio/mp3", start_time=0)
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# For download button, we still need to read the bytes
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with open(result["audio_path"], "rb") as audio_file:
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audio_bytes = audio_file.read()
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st.download_button(
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label="📥 Download Audio Response",
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data=audio_bytes,
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file_name=f"voice_response_{st.session_state.selected_voice}.mp3",
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mime="audio/mp3"
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)
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st.markdown("### Sources:")
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for source in result["sources"]:
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st.markdown(f"- {source}")
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else:
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status.update(label="❌ Error processing query", state="error")
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st.error(f"Error: {result.get('error', 'Unknown error occurred')}")
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except Exception as e:
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status.update(label="❌ Error processing query", state="error")
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st.error(f"Error processing query: {str(e)}")
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elif not st.session_state.setup_complete:
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st.info("👈 Please configure the system using the sidebar first!")
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if __name__ == "__main__":
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asyncio.run(main())
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run_streamlit()
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