FirstCommit

app.py

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