app.py

"""
@author: idoia lerchundi
"""
import os
import time
import streamlit as st
from huggingface_hub import InferenceClient
import random

# Load the API token from an environment variable
api_key = os.getenv("HF_TOKEN")

# Instantiate the InferenceClient
client = InferenceClient(api_key=api_key)

# Function to simulate some process and return the elapsed time
def process_with_timing(): start_time = time.time() 
    # Simulate a process with sleep
    time.sleep(2.345) 
    # Change this value to simulate different processing times
    end_time = time.time() 
    elapsed_time = end_time - start_time
    minutes, seconds = divmod(elapsed_time, 60)
    milliseconds = (seconds - int(seconds)) * 1000
    return minutes, int(seconds), milliseconds

# Streamlit app title
st.title("Text-generation model using Streamlit from Inference API (serverless) feature.") 

# Ensure the full_text key is initialized in session state
if "full_text" not in st.session_state:
    st.session_state["full_text"] = ""

# Model selection dropdown
model_options = [ "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "gpt2", "facebook/opt-1.3b", "EleutherAI/gpt-neo-2.7B","meta-llama/Llama-Llama-3-8B-Instruct", "meta-llama/Llama-Llama-3.1-1B-Instruct", "meta-llama/Llama-Llama-3.2-3B-Instruct", "meta-llama/Llama-Llama-3.2-8B-Instruct", "Qwen/Qwen2.5-1.5B-Instruct", "openai-community/gpt2", "google/gemma-1.1-7b-it", "google/gemma-1.27b-it", "google/gemma-1.2b-it", "google/gemma-1.9b-it", "google/gemma-2.2b-it", "HuggingFaceH4/starchat7b-beta", "distilbert/distilgpt2", "facebook/opt-1.3b", "distributed/optimized=gpt2-1b" ]
selected_model = st.selectbox("Choose a model:", model_options)

# Create a text input area for user prompts
with st.form("my_form"):
    text = st.text_area("JOKER (TinyLlama is not great at joke telling.) (using model TinyLlama/TinyLlama-1.1B-Chat-v1.0):", "Tell me a clever and funny joke in exactly 4 sentences. It should make me laugh really hard. Don't repeat the topic in your joke. Be creative and concise.")
    submitted = st.form_submit_button("Submit")

# Initialize the full_text variable
full_text = " "

# Generate a random temperature between 0.5 and 1.0
temperature = random.uniform(0.5, 1.0)

if submitted:
    messages = [
        {"role": "user", "content": text}
    ]
    
    # Create a new stream for each submission
    stream = client.chat.completions.create(
        model=selected_model,
        messages=messages,
        # Generate a random temperature between 0.5 and 1.0
        temperature = random.uniform(0.5, 1.0),
        max_tokens=300,
        top_p=random.uniform(0.7, 1.0),
        stream=True
    )
    minutes, seconds, milliseconds = process_with_timing()
    st.write(f"Elapsed Time: {int(minutes)} minutes, {seconds} seconds, and {milliseconds:.2f} milliseconds")
    
    # 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
if st.session_state["full_text"]:
    st.info(st.session_state["full_text"])