Create app.py

app.py

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+import requests
+from PIL import Image
+from io import BytesIO
+import base64
+import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+import numpy as np
+
+# Replace with your own API key
+STABLE_DIFFUSION_API_KEY = "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"
+
+# Load the CLIP model and processor
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+def get_mood_from_image(image: Image.Image):
+    moods = ["fear", "anger", "joy", "sadness", "disgust", "surprise"]
+    
+    # Create unique prompts for each mood
+    prompts = [
+        "The emotion conveyed by this image is fear. The person looks scared and tense.",
+        "The emotion conveyed by this image is anger. The person looks furious and irritated.",
+        "The emotion conveyed by this image is joy. The person looks happy and cheerful.",
+        "The emotion conveyed by this image is sadness. The person looks unhappy and gloomy.",
+        "The emotion conveyed by this image is disgust. The person looks repulsed and sickened.",
+        "The emotion conveyed by this image is surprise. The person looks astonished and amazed.",
+    ]
+    
+    # Prepare the inputs for the model
+    inputs = processor(text=prompts, images=image, return_tensors="pt", padding=True)
+    
+    # Run the model
+    logits = model(**inputs).logits_per_image
+    probs = logits.softmax(dim=-1).tolist()
+
+    # Calculate the scores for each mood
+    mood_scores = {}
+    for mood, score in zip(moods, probs[0]):
+        mood_scores[mood] = score
+    print("Mood Scores:", mood_scores)
+    # Select the mood with the highest score
+    selected_mood = max(mood_scores, key=mood_scores.get)
+
+    return selected_mood
+
+def generate_art(mood):
+    # Implement art generation logic using the Stable Diffusion API
+    prompt = f"{mood} generative art with vibrant colors and intricate patterns"
+    ...
+
+    headers = {
+        "Authorization": f"Bearer {STABLE_DIFFUSION_API_KEY}",
+        "Accept": "image/jpeg",  # Set the Accept header to receive an image directly
+    }
+
+    json_data = {
+        "inputs": prompt
+    }
+
+    response = requests.post('https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5', headers=headers, json=json_data)
+
+    # Check if the response status is not 200 (OK)
+    if response.status_code != 200:
+        print(f"Error: API response status code {response.status_code}")
+        print("Response content:")
+        print(response.content)
+        return None
+
+    # Load the image directly from the response content
+    image = Image.open(BytesIO(response.content))
+
+    return image
+
+mood_emojis = {
+    "fear": "😨",
+    "anger": "😠",
+    "joy": "😄",
+    "sadness": "😢",
+    "disgust": "🤢",
+    "surprise": "😮",
+}
+
+def mood_art_generator(image):
+    mood = get_mood_from_image(image)
+    print("Mood:", mood)
+    if mood:
+        art = generate_art(mood)
+        emoji = mood_emojis[mood
+        output_text = f"You seem to be {mood} {emoji}. Here's an artwork representing it!"
+        return art, output_text
+    else:
+        return None, "Failed to generate artwork."
+
+iface = gr.Interface(
+    fn=mood_art_generator,
+    inputs=gr.inputs.Image(shape=(224, 224), image_mode="RGB", source="upload"),
+    outputs=[gr.outputs.Image(type="pil"), gr.outputs.Textbox()],
+    title="Mood-based Art Generator",
+    description="Upload an image of yourself and let the AI generate artwork based on your mood.",
+    allow_flagging=False,
+    analytics_enabled=False
+)
+
+iface.launch(share=True)