Delete Generate.py

Generate.py

@@ -1,95 +0,0 @@
-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
-
-def main():
-    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.",
-    )
-
-    interface.launch()
-
-if __name__ == "__main__":
-    main()