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| """ | |
| @author: idoia lerchundi | |
| """ | |
| import os | |
| import streamlit as st | |
| from huggingface_hub import InferenceClient | |
| # Load the API token from an environment variable | |
| api_key = os.getenv("HF_TOKEN") | |
| # Instantiate the InferenceClient | |
| client = InferenceClient(api_key=api_key) | |
| # Streamlit app title | |
| st.title("Hugging Face Inference with Streamlit") | |
| # Create a text input area for user prompts | |
| with st.form("my_form"): | |
| text = st.text_area("Enter text:", "Tell me a joke to make me laugh.") | |
| submitted = st.form_submit_button("Submit") | |
| # Initialize the full_text variable | |
| full_text = "" | |
| if submitted: | |
| messages = [ | |
| {"role": "user", "content": text} | |
| ] | |
| # Create a new stream for each submission | |
| stream = client.chat.completions.create( | |
| model="TinyLlama/TinyLlama-1.1B-Chat-v1.0", | |
| messages=messages, | |
| temperature=0.5, | |
| max_tokens=300, | |
| top_p=0.7, | |
| stream=True | |
| ) | |
| # Concatenate chunks to form the full response | |
| for chunk in stream: | |
| full_text += chunk.choices[0].delta.content | |
| # Update session state with the full response | |
| st.session_state["full_text"] = full_text | |
| # Display the full response | |
| st.info(full_text) | |
| # Display the full response | |
| if st.session_state["full_text"]: | |
| st.info(st.session_state["full_text"]) |