README.md

metadata

title: Deepseek Chat
emoji: 👀
colorFrom: yellow
colorTo: pink
sdk: streamlit
sdk_version: 1.41.1
app_file: app.py
pinned: true
license: apache-2.0
short_description: deepseek chat - huggingface api

Below is the content for a README.md file that you can include in your project to provide an overview of the code, its requirements, and how to use it:

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🐳 Chat with DeepSeek 🐳

This is a Streamlit-based web application that allows users to interact with the DeepSeek-R1-Distill-Qwen-32B model hosted on Hugging Face. The application uses the Hugging Face Inference API to generate responses to user queries.

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🧑‍💻 Author

Sachin Tiwari

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🚀 Features

• Interactive Chat Interface: Users can input questions and receive responses from the DeepSeek model.
• Thinking Process Visualization: If the model's response contains <think> tags, the app displays the thinking process in an expandable section.
• Secure API Key Input: Users can securely input their Hugging Face API key to authenticate and use the model.

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📋 Requirements

To run this application, you need the following Python packages:

• streamlit: For building the web interface.
• huggingface_hub: For interacting with the Hugging Face Inference API.

You can install the required packages using the following command:

pip install streamlit huggingface_hub

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🛠️ How to Use

1. Clone the Repository:

   git clone <https://github.com/aitiwari/Deepseek_Chat_HF.git>
   cd <Deepseek_Chat_HF>
   

2. Run the Application: Start the Streamlit app by running the following command:

   streamlit run app.py
   

3. Enter Your Hugging Face API Key:

   • Open the app in your browser.
   • In the sidebar, enter your Hugging Face API key in the provided input box.

4. Chat with DeepSeek:

   • Once the API key is entered, you can start chatting with the DeepSeek model by typing your questions in the chat input box.

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🧑‍💻 Code Overview

The application is built using the following components:

• Streamlit UI: The interface is created using Streamlit, with a sidebar for API key input and a main area for chat interactions.
• Hugging Face Inference API: The InferenceClient from the huggingface_hub library is used to send requests to the DeepSeek model.
• Response Parsing: The app checks for <think> tags in the model's response and displays the thinking process separately.

Here’s the main code snippet:

import streamlit as st
from huggingface_hub import InferenceClient

# Streamlit UI
st.title("🐳 Chat with DeepSeek 🐳")

with st.sidebar:
    # Input box for user to enter their Hugging Face API key
    api_key = st.text_input("Enter your Hugging Face API Key:", type="password")

if api_key:
    # Initialize the InferenceClient with the user-provided API key
    client = InferenceClient(api_key=api_key)

    # Input box for user to enter their question
    user_input = st.chat_input("Enter your question:")
    

    if user_input:
        # Prepare the messages for the model
        messages = [
            {
                "role": "user",
                "content": user_input
            }
        ]
        with st.chat_message("user"):
            st.write(user_input)

        # Get the completion from the model
        completion = client.chat.completions.create(
            model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", 
            messages=messages, 
        )

        # Get the model's response
        response = completion.choices[0].message['content']

        # Check if the response contains <think> tags
        if "<think>" in response and "</think>" in response:
            # Extract content within <think> tags
            think_content = response.split("<think>")[1].split("</think>")[0].strip()
            # Display the thinking content in an expander
            with st.expander("Thinking..."):
                st.write(think_content)

            # Extract the rest of the response (outside <think> tags)
            rest_of_response = response.split("</think>")[1].strip()
            # Display the rest of the response with an AI icon
            with st.chat_message("ai"):
                st.write(rest_of_response)
        else:
            # If no <think> tags, display the entire response with an AI icon
            with st.chat_message("ai"):
                st.write(rest_of_response)
else:
    with st.sidebar:
        st.warning("Please enter your Hugging Face API Key to proceed.")

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Output

• Huggingface Space: Link

📝 Notes

• API Key Security: Ensure that your Hugging Face API key is kept secure and not shared publicly.
• Model Limitations: The responses generated by the DeepSeek model are based on its training data and may not always be accurate or complete.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.

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