DeepSeek-R1 on Groq Chat

https://github.com/zwelshman/chat_streamlit
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A free web-based chat application that provides access to DeepSeek-R1 language model through Groq's...
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### Install Dependencies and Setup Groq API Key

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Installs necessary Python packages and configures the Groq API key using Streamlit's secrets management.

```bash
pip install streamlit groq

mkdir -p .streamlit
echo 'GROQ_API_KEY = "your-groq-api-key-here"' > .streamlit/secrets.toml

streamlit run app.py
```

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### Initialize Groq Client with Streamlit Secrets

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Initializes the Groq client using the API key securely stored in Streamlit's secrets.toml file.

```python
import streamlit as st
from groq import Groq

client = Groq(api_key=st.secrets["GROQ_API_KEY"])
```

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### Generate AI Response with DeepSeek-R1 via Groq API

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Generates an AI response using the DeepSeek-R1 model through the Groq chat completions API.

```python
from groq import Groq
import streamlit as st

client = Groq(api_key=st.secrets["GROQ_API_KEY"])

response = client.chat.completions.create(
    model="deepseek-r1-distill-llama-70b",
    messages=[
        {"role": "user", "content": "Explain quantum computing in simple terms"}
    ],
    stream=False
)

full_response = response.choices[0].message.content
print(full_response)
```

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### Configure Streamlit Page Title and Icon

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Sets the browser tab title and the main title displayed on the Streamlit application page.

```python
import streamlit as st

st.set_page_config(page_title="Free DeepSeek Chat", page_icon="🤖")
st.title("DeepSeek-R1 on Groq (Free)")
```

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### Handle User Input with Streamlit Chat Input

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Captures user input using Streamlit's chat input widget and adds it to the conversation history and UI.

```python
import streamlit as st

if prompt := st.chat_input("Ask DeepSeek anything..."):
    st.session_state.messages.append({"role": "user", "content": prompt})

    with st.chat_message("user"):
        st.markdown(prompt)
```

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### Manage Chat History with Streamlit Session State

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Initializes and manages the conversation history using Streamlit's session state to maintain context across user interactions.

```python
import streamlit as st

if "messages" not in st.session_state:
    st.session_state.messages = []

st.session_state.messages.append({"role": "user", "content": "Hello, how are you?"})
st.session_state.messages.append({"role": "assistant", "content": "I'm doing well, thank you!"})

for message in st.session_state.messages:
    print(f"{message['role']}: {message['content']}")
```

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### Maintain Context in Multi-turn Conversations

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Sends the entire conversation history to the Groq API to enable multi-turn interactions and maintain context.

```python
from groq import Groq

client = Groq(api_key="your-api-key")

messages = [
    {"role": "user", "content": "What is Python?"},
    {"role": "assistant", "content": "Python is a high-level programming language..."},
    {"role": "user", "content": "What are its main use cases?"}
]

response = client.chat.completions.create(
    model="deepseek-r1-distill-llama-70b",
    messages=[
        {"role": m["role"], "content": m["content"]} 
        for m in messages
    ],
    stream=False
)

print(response.choices[0].message.content)
```

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### Display Chat History in Streamlit UI

Source: https://context7.com/zwelshman/chat_streamlit/llms.txt

Renders the conversation history within Streamlit's chat message containers for a clear user interface.

```python
import streamlit as st

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])
```
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