Update 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
+
+# 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 = ["happy", "sad", "angry", "neutral"]
+    prompt = "The mood of the person in this image is: "
+    
+    # Convert the image to base64
+    buffered = BytesIO()
+    image.save(buffered, format="JPEG")
+    image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
+
+    # Create text prompts for each mood
+    text_inputs = [f"{prompt}{mood}" for mood in moods]
+    
+    # Prepare the inputs for the model
+    inputs = processor(text=text_inputs, images=image, return_tensors="pt", padding=True)
+    
+    # Run the model
+    logits = model(**inputs).logits
+    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
+
+    # Filter moods with a score above 60%
+    filtered_moods = {k: v for k, v in mood_scores.items() if v > 0.6}
+
+    if len(filtered_moods) < 2:
+        return None
+
+    # Select the mood with the highest score
+    selected_mood = max(filtered_moods, key=filtered_moods.get)
+    return selected_mood
+
+def generate_art(mood):
+    # Implement art generation logic using the Stable Diffusion API
+    prompt = f"{mood} generative art"
+
+    headers = {
+        "Authorization": f"Bearer {STABLE_DIFFUSION_API_KEY}"
+    }
+
+    json_data = {
+        "inputs": prompt
+    }
+
+    response = requests.post('https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5', headers=headers, json=json_data).json()
+
+    # Extract the generated image URL from the response
+    image_url = response["generated_images"][0]["url"]
+
+    # Download the image and return it as a PIL Image
+    image_response = requests.get(image_url)
+    image = Image.open(BytesIO(image_response.content))
+
+    return image
+
+def mood_art_generator(image: Image.Image):
+    mood = get_mood_from_image(image)
+    if mood:
+        art = generate_art(mood)
+        return art
+    else:
+        return None
+
+image_input = gr.inputs.Image()
+outputs = gr.outputs.Image(label="Generated Artwork")
+
+interface = gr.Interface(
+    fn=mood_art_generator,
+    inputs=image_input,
+    outputs=outputs,
+    title="Mood-based Art Generator",
+    description="Upload an image of yourself and let the AI generate artwork based on your mood.",
+)
+
+if __name__ == "__main__":
+    interface.launch()