Trying to better it

app.py

@@ -15,14 +15,20 @@ 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: "
+    moods = ["fear", "anger", "joy", "sadness", "disgust", "surprise"]
     
-    # Create text prompts for each mood
-    text_inputs = [f"{prompt}{mood}" for mood in moods]
+    # 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=text_inputs, images=image, return_tensors="pt", padding=True)
+    inputs = processor(text=prompts, images=image, return_tensors="pt", padding=True)
     
     # Run the model
     logits = model(**inputs).logits_per_image
@@ -40,10 +46,9 @@ def get_mood_from_image(image: Image.Image):
 
 def generate_art(mood):
     # Implement art generation logic using the Stable Diffusion API
-    prompt = f"{mood} generative art with vibrant colors and intricate patterns"
-    ...
-
-    request_headers = {
+    prompt = f"{mood} generative art with vibrant colors and intricate patterns ({str(np.random.randint(1, 10000))})"
+    
+    headers = {
         "Authorization": f"Bearer {STABLE_DIFFUSION_API_KEY}",
         "Accept": "image/jpeg",  # Set the Accept header to receive an image directly
     }
@@ -53,11 +58,9 @@ def generate_art(mood):
     }
 
     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)
+        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 == 503:  # Model is loading
+        if response.status_code == 503:
             print("Model is loading, waiting for 30 seconds before retrying...")
             time.sleep(30)
             continue
@@ -70,24 +73,34 @@ def generate_art(mood):
 
         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)
-        return art
+        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
+        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"),
+    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,
@@ -96,3 +109,4 @@ iface = gr.Interface(
 )
 
 iface.launch()
+