Create app.py

app.py

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+import spaces
+import gradio as gr
+import re
+import os 
+hf_token = os.environ.get('HF_TOKEN')
+
+from gradio_client import Client, handle_file
+
+clipi_client = Client("fffiloni/CLIP-Interrogator-2")
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_path = "meta-llama/Llama-2-7b-chat-hf"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, use_auth_token=hf_token)
+model = AutoModelForCausalLM.from_pretrained(model_path, use_auth_token=hf_token).half().cuda()
+
+#client = Client("https://fffiloni-test-llama-api-debug.hf.space/", hf_token=hf_token)
+
+clipi_client = Client("https://fffiloni-clip-interrogator-2.hf.space/")
+
+@spaces.GPU
+def llama_gen_fragrance(scene):
+
+    instruction = f"""[INST] <<SYS>>\n
+You are a poetic perfumer. Your role is to create the imaginary scent of a described scene.
+
+You must always respond using the following structure:
+
+---
+
+**Perfume Name**:  
+[An original, evocative, and unique name — in French or English]
+
+**Tagline**:  
+[A short, poetic sentence — like a perfume advertisement hook]
+
+**Poetic Olfactory Description**:  
+[A freeform and expressive description of the scent ambiance evoked by the scene. Use sensory, emotional, and evocative language. You may include metaphors, seasonal references, or mood imagery.]
+
+**Olfactory Pyramid (technical)**:
+
+- **Top Notes**: [3–4 precise and vivid elements. Example: frozen pine, mandarin peel, cold ozone]  
+- **Heart Notes**: [3–4 elements suggesting warmth or the body of the fragrance. Example: smoked wood, soft leather, toasted bread]  
+- **Base Notes**: [3–4 deeper, earthier, or longer-lasting elements. Example: white musk, ash, ambergris]
+
+**General Atmosphere**:  
+[A final synthesis that summarizes the overall impression of the fragrance — contrast, duration, evolution, or emotional tone. Keep it artistic.]
+
+---
+
+Generate one single imaginary perfume following these instructions.
+Here is the scene to analyze:  
+\n<</SYS>>\n\n{} [/INST]"""
+
+    
+    prompt = instruction.format(scene)
+    
+    generate_ids = model.generate(tokenizer(prompt, return_tensors='pt').input_ids.cuda(), max_new_tokens=4096)
+    output_text = tokenizer.decode(generate_ids[0], skip_special_tokens=True)
+    #print(generate_ids)
+    #print(output_text)
+    pattern = r'\[INST\].*?\[/INST\]'
+    cleaned_text = re.sub(pattern, '', output_text, flags=re.DOTALL)
+    return cleaned_text
+
+def get_text_after_colon(input_text):
+    # Find the first occurrence of ":"
+    colon_index = input_text.find(":")
+    
+    # Check if ":" exists in the input_text
+    if colon_index != -1:
+        # Extract the text after the colon
+        result_text = input_text[colon_index + 1:].strip()
+        return result_text
+    else:
+        # Return the original text if ":" is not found
+        return input_text
+
+def infer(image_input, audience):
+
+    gr.Info('Calling CLIP Interrogator ...')
+
+    clipi_result = clipi_client.predict(
+		image=handle_file(image_input),
+		mode="best",
+		best_max_flavors=4,
+		api_name="/clipi2"
+    )
+    print(clipi_result)
+   
+
+    llama_q = clipi_result
+    
+    
+    gr.Info('Calling Llama2 ...')
+    result = llama_gen_fragrance(llama_q)
+
+    print(f"Llama2 result: {result}")
+
+    result = get_text_after_colon(result)
+
+    # Split the text into paragraphs based on actual line breaks
+    paragraphs = result.split('\n')
+    
+    # Join the paragraphs back with an extra empty line between each paragraph
+    formatted_text = '\n\n'.join(paragraphs)
+
+
+    return formatted_text
+
+css="""
+#col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
+div#fragrance textarea {
+    font-size: 1.5em;
+    line-height: 1.4em;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            """
+            <h1 style="text-align: center">Image to Fragrance</h1>
+            <p style="text-align: center">Upload an image, get a pro fragrance idea made by Llama2 !</p>
+            """
+        )
+        with gr.Row():
+            with gr.Column():
+                image_in = gr.Image(label="Image input", type="filepath", elem_id="image-in")
+                
+                submit_btn = gr.Button('Tell me a story')
+            with gr.Column():
+                #caption = gr.Textbox(label="Generated Caption")
+                fragrance = gr.Textbox(label="generated Fragrance", elem_id="fragrance")
+        
+        gr.Examples(examples=[["./examples/crabby.png", "Children"],["./examples/hopper.jpeg", "Adult"]],
+                    fn=infer,
+                    inputs=[image_in],
+                    outputs=[fragrance],
+                    cache_examples=False
+                   )
+        
+    submit_btn.click(fn=infer, inputs=[image_in], outputs=[fragrance])
+
+demo.queue(max_size=12).launch(ssr_mode=False, mcp_server=True)