import streamlit as st
import cohere
import os
import base64

st.set_page_config(page_title="Cohere Chat", layout="wide")

AI_PFP = "media/pfps/cohere-pfp.png"
USER_PFP = "media/pfps/user-pfp.jpg"
BANNER = "media/banner.png"

model_info = {
    "c4ai-aya-expanse-8b": {"description": "Aya Expanse is a highly performant 8B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages.", "context": "4K", "output": "4K"},
    "c4ai-aya-expanse-32b": {"description": "Aya Expanse is a highly performant 32B multilingual model, designed to rival monolingual performance through innovations in instruction tuning with data arbitrage, preference training, and model merging. Serves 23 languages.", "context": "128K", "output": "4K"},
    "command-a-03-2025": {"description": "Command A is our most performant model to date, excelling at tool use, agents, retrieval augmented generation (RAG), and multilingual use cases. Command A has a context length of 256K, only requires two GPUs to run, and has 150% higher throughput compared to Command R+ 08-2024.", "context": "256K", "output": "8K"},
    "command-r7b-12-2024": {"description": "command-r7b-12-2024 is a small, fast update delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning and multiple steps.", "context": "128K", "output": "4K"},
    "command-r-plus-04-2024": {"description": "Command R+ is an instruction-following conversational model that performs language tasks at a higher quality, more reliably, and with a longer context than previous models. It is best suited for complex RAG workflows and multi-step tool use.", "context": "128K", "output": "4K"},
    "command-r-plus": {"description": "command-r-plus is an alias for command-r-plus-04-2024, so if you use command-r-plus in the API, that's the model you're pointing to.", "context": "128K", "output": "4K"},
    "command-r-08-2024": {"description": "Updated Command R model from August 2024.", "context": "128K", "output": "4K"},
    "command-r-03-2024": {"description": "Instruction-following model for code generation, RAG, and agents.", "context": "128K", "output": "4K"},
    "command-r": {"description": "Alias for command-r-03-2024.", "context": "128K", "output": "4K"},
    "command": {"description": "Conversational model with long context capabilities.", "context": "4K", "output": "4K"},
    "command-nightly": {"description": "Experimental nightly build (not for production).", "context": "128K", "output": "4K"},
    "command-light": {"description": "Faster lightweight version of command.", "context": "4K", "output": "4K"},
    "command-light-nightly": {"description": "Experimental nightly build of command-light.", "context": "128K", "output": "4K"},
    "c4ai-aya-vision-8b": {"description": "Aya Vision is an 8B vision-language model enabling image-based chat and analysis.", "context": "4K", "output": "4K"},
    "c4ai-aya-vision-32b": {"description": "Aya Vision is a 32B vision-language model with advanced image understanding and reasoning.", "context": "128K", "output": "4K"}
}

with st.sidebar:
    st.image(BANNER, use_container_width=True)
    st.title("Settings")
    api_key = st.text_input("Cohere API Key", type="password")
    selected_model = st.selectbox("Model", options=list(model_info.keys()))
    if selected_model.startswith("c4ai-aya-vision"):
        uploaded = st.file_uploader("Upload image", type=["png","jpg","jpeg"])
        if uploaded:
            data = uploaded.read()
            session_image = base64.b64encode(data).decode('utf-8')
            st.session_state.image_data = session_image
    if st.button("Clear Chat"):
        st.session_state.messages = []
        st.session_state.first_message_sent = False
        st.session_state.image_data = None
        st.rerun()
    st.divider()
    st.subheader(selected_model)
    st.markdown(model_info[selected_model]["description"])
    st.caption(f"Context: {model_info[selected_model]['context']}")
    st.caption(f"Output: {model_info[selected_model]['output']}")
    st.markdown("Powered by Cohere's API")

if "messages" not in st.session_state:
    st.session_state.messages = []
if "first_message_sent" not in st.session_state:
    st.session_state.first_message_sent = False
if "image_data" not in st.session_state:
    st.session_state.image_data = None

if not st.session_state.first_message_sent:
    st.markdown("<h1 style='text-align: center; color: #4a4a4a; margin-top: 100px;'>How can Cohere help you today?</h1>", unsafe_allow_html=True)
for msg in st.session_state.messages:
    with st.chat_message(msg["role"], avatar=USER_PFP if msg["role"]=="user" else AI_PFP):
        content = msg["content"]
        if isinstance(content, list):
            for item in content:
                if item.get("type")=="text":
                    st.markdown(item.get("text"))
                if item.get("type")=="image_url":
                    st.image(item.get("image_url").get("url"))
        else:
            st.markdown(content)
if prompt := st.chat_input("Message..."):
    if not api_key:
        st.error("API key required")
        st.stop()
    st.session_state.first_message_sent = True
    st.session_state.messages.append({"role":"user","content":prompt})
    with st.chat_message("user", avatar=USER_PFP):
        st.markdown(prompt)
    co = cohere.ClientV2(api_key)
    content = [{"type":"text","text":prompt}]
    if st.session_state.image_data and selected_model.startswith("c4ai-aya-vision"):
        data_url = f"data:image/jpeg;base64,{st.session_state.image_data}"
        content.append({"type":"image_url","image_url":{"url":data_url}})
    response = co.chat(model=selected_model, messages=[*st.session_state.messages, {"role":"user","content":content}], temperature=0.3)
    items = response.message.content
    reply = "".join([getattr(i,'text','') for i in items])
    with st.chat_message("assistant", avatar=AI_PFP):
        st.markdown(reply)
    st.session_state.messages.append({"role":"assistant","content":items})