Update app.py

app.py

@@ -5,100 +5,93 @@ import base64
 import gradio as gr
 from transformers import CLIPProcessor, CLIPModel
 import numpy as np
+import time
 
 # 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"]
+    moods = ["happy", "sad", "angry", "neutral"]
+    prompt = "The mood of the person in this image is: "
     
-    # 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.",
-    ]
+    # Create text prompts for each mood
+    text_inputs = [f"{prompt}{mood}" for mood in moods]
+
+
+
+
+
+
+
     
     # Prepare the inputs for the model
-    inputs = processor(text=prompts, images=image, return_tensors="pt", padding=True)
+    inputs = processor(text=text_inputs, 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 = {
+    request_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)
+    while True:
+        # Use the correct variable name (request_headers) in the post request
+        response = requests.post('https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5', headers=request_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
+        # Check if the response status is not 200 (OK)
+        if response.status_code == 503:  # Model is loading
+            print("Model is loading, waiting for 30 seconds before retrying...")
+            time.sleep(30)
+            continue
+
+        if response.status_code != 200:
+            print(f"Error: API response status code {response.status_code}")
+            print("Response content:")
+            print(response.content)
+            return None
+
+        break
 
     # 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
+        return art
+
+
     else:
-        return None, "Failed to generate artwork."
+        return None
 
 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()],
+    outputs=gr.outputs.Image(type="pil"),
     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
+    analytics_enabled=False,
+    share=True
 )
 
-iface.launch(share=True)
+iface.launch()