Update app.py
Browse files
app.py
CHANGED
@@ -1,195 +1,66 @@
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import streamlit as st
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import mediapipe as mp
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import numpy as np
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import base64
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import io
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import PIL.Image
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import asyncio
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import os
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import
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from
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#
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#
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client = genai.Client(http_options={"api_version": "v1alpha"})
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MODEL = "models/gemini-2.0-flash-exp"
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CONFIG = {"generation_config": {"response_modalities": ["AUDIO"]}}
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def audio_callback(self, indata, frames, time, status):
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"""This is called (from a separate thread) for each audio block."""
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if status:
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print(status)
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self.audio_queue.put_nowait(indata.copy())
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def start_stream(self):
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try:
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self.stream = sd.InputStream(
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channels=CHANNELS,
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samplerate=SAMPLE_RATE,
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callback=self.audio_callback,
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blocksize=CHUNK_SIZE
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)
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self.stream.start()
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except Exception as e:
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st.error(f"Error starting audio stream: {str(e)}")
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self.stream.stop()
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self.stream.close()
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self.stream = None
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self.frame_queue = asyncio.Queue(maxsize=5)
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self.mp_draw = mp.solutions.drawing_utils
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self.mp_face_detection = mp.solutions.face_detection
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self.face_detection = self.mp_face_detection.FaceDetection(
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min_detection_confidence=0.5)
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def video_frame_callback(self, frame):
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img = frame.to_ndarray(format="rgb24")
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pil_img = PIL.Image.fromarray(img)
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pil_img.thumbnail([1024, 1024])
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image_io = io.BytesIO()
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pil_img.save(image_io, format="jpeg")
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image_io.seek(0)
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frame_data = {
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"mime_type": "image/jpeg",
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"data": base64.b64encode(image_io.read()).decode()
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}
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try:
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self.frame_queue.put_nowait(frame_data)
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except asyncio.QueueFull:
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pass
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def __del__(self):
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if hasattr(self, 'face_detection'):
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self.face_detection.close()
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if 'video_processor' not in st.session_state:
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st.session_state.video_processor = VideoProcessor()
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if 'session' not in st.session_state:
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st.session_state.session = None
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if 'messages' not in st.session_state:
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st.session_state.messages = []
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def display_chat_messages():
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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def main():
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st.title("Gemini Interactive Assistant")
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initialize_session_state()
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)
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enable_face_detection = st.sidebar.checkbox("Enable Face Detection", value=True)
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if enable_face_detection:
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detection_confidence = st.sidebar.slider(
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"Face Detection Confidence",
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min_value=0.0,
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max_value=1.0,
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value=0.5,
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step=0.1
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)
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st.session_state.video_processor.face_detection = (
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st.session_state.video_processor.mp_face_detection.FaceDetection(
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min_detection_confidence=detection_confidence
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)
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)
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display_chat_messages()
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if input_mode == "Text Only":
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user_input = st.chat_input("Your message")
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if user_input:
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st.session_state.messages.append({"role": "user", "content": user_input})
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with st.chat_message("user"):
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st.markdown(user_input)
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async def send_message():
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async with client.aio.live.connect(model=MODEL, config=CONFIG) as session:
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await session.send(user_input, end_of_turn=True)
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turn = session.receive()
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async for response in turn:
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if text := response.text:
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st.session_state.messages.append(
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{"role": "assistant", "content": text}
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)
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with st.chat_message("assistant"):
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st.markdown(text)
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asyncio.run(send_message())
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else:
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if input_mode == "Audio + Video":
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ctx = webrtc_streamer(
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key="gemini-stream",
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video_frame_callback=st.session_state.video_processor.video_frame_callback,
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rtc_configuration={"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]},
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media_stream_constraints={"video": True, "audio": True},
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)
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col1, col2 = st.columns(2)
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with col1:
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if st.button("Start Recording", type="primary"):
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st.session_state.audio_processor.start_stream()
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st.session_state['recording'] = True
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with col2:
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if st.button("Stop Recording", type="secondary"):
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st.session_state.audio_processor.stop_stream()
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st.session_state['recording'] = False
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async def process_audio_stream():
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while st.session_state.get('recording', False):
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try:
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audio_data = await st.session_state.audio_processor.audio_queue.get()
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await st.session_state.audio_processor.audio_queue.put({
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"data": audio_data.tobytes(),
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"mime_type": "audio/pcm",
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"sample_rate": SAMPLE_RATE
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})
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except asyncio.QueueEmpty:
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pass
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await asyncio.sleep(0.1)
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if
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import os
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import streamlit as st
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import google.generativeai as genai
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from PIL import Image
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# Set up the Streamlit App
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st.set_page_config(page_title="Multimodal Chatbot with Gemini Flash", layout="wide")
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st.title("Multimodal Chatbot with Gemini Flash ⚡️")
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st.caption("Chat with Google's Gemini Flash model using image and text input to get lightning fast results. 🌟")
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# Get OpenAI API key from user
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api_key = "AIzaSyC_zxN9IHjEAxIoshWPzMfgb9qwMsu5t5Y"
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# Set up the Gemini model
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genai.configure(api_key=api_key)
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model = genai.GenerativeModel(model_name="gemini-1.5-flash-latest")
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if api_key:
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# Initialize the chat history
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Sidebar for image upload
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with st.sidebar:
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st.title("Chat with Images")
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uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
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if uploaded_file:
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image = Image.open(uploaded_file)
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st.image(image, caption='Uploaded Image', use_column_width=True)
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# Main layout
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chat_placeholder = st.container()
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with chat_placeholder:
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# Display the chat history
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for message in st.session_state.messages:
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with st.chat_message(message["role"]):
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st.markdown(message["content"])
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# User input area at the bottom
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prompt = st.chat_input("What do you want to know?")
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if prompt:
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inputs = [prompt]
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# Add user message to chat history
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st.session_state.messages.append({"role": "user", "content": prompt})
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# Display user message in chat message container
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with chat_placeholder:
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with st.chat_message("user"):
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st.markdown(prompt)
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if uploaded_file:
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inputs.append(image)
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with st.spinner('Generating response...'):
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# Generate response
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response = model.generate_content(inputs)
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# Display assistant response in chat message container
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with chat_placeholder:
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with st.chat_message("assistant"):
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st.markdown(response.text)
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if uploaded_file and not prompt:
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st.warning("Please enter a text query to accompany the image.")
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