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import streamlit as st |
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from transformers import pipeline |
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from huggingface_hub import InferenceClient |
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from PIL import Image |
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import os |
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def setup_session(): |
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if 'app_ready' not in st.session_state: |
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print("Powering up the Dragon Radar...") |
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st.session_state['app_ready'] = True |
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st.session_state['hf_token'] = os.getenv("HUGGINGFACE_TOKEN") |
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st.session_state['client'] = InferenceClient(api_key=st.session_state['hf_token']) |
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def main(): |
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setup_session() |
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st.header("Anime & Friends Image Commentary") |
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st.write("Let your favorite characters react to any image!") |
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character = st.selectbox( |
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"Select your commentator", |
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["goku", "elmo", "kirby", "pikachu"] |
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) |
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uploaded_img = st.file_uploader("Share your image!") |
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if uploaded_img is not None: |
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image = Image.open(uploaded_img) |
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st.image(image) |
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caption_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large") |
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base_caption = caption_model(image)[0]['generated_text'] |
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character_reactions = { |
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"goku": f"Describe this image like you're Goku from Dragon Ball Z, mentioning power levels: {base_caption}", |
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"elmo": f"Describe this image like you're Elmo from Sesame Street, speaking in third person: {base_caption}", |
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"kirby": f"Describe this image like you're Kirby, being cute and mentioning food: {base_caption}", |
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"pikachu": f"Describe this image like you're Pikachu, using 'pika' frequently: {base_caption}" |
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} |
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messages = [ |
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{ |
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"role": "user", |
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"content": character_reactions[character] |
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} |
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] |
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response_stream = st.session_state['client'].chat.completions.create( |
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model="meta-llama/Llama-3.2-3B-Instruct", |
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messages=messages, |
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max_tokens=500, |
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stream=True |
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) |
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character_response = '' |
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for chunk in response_stream: |
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character_response += chunk.choices[0].delta.content |
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st.write(character_response) |
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if __name__ == '__main__': |
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main() |
