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README.md

@@ -1,14 +1,14 @@
----
-title: ClipArte
-emoji: 🏃
-colorFrom: yellow
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.44.1
-app_file: app.py
-pinned: false
-license: cc-by-nc-sa-3.0
-short_description: Uso di clip con immagini di esempio
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: ClipArte
+emoji: 🏃
+colorFrom: yellow
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+license: cc-by-nc-sa-3.0
+short_description: Uso di clip con immagini di esempio
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,69 @@
+import pandas as pd
+import torch
+from transformers import CLIPProcessor, CLIPModel
+from sklearn.metrics.pairwise import cosine_similarity
+from PIL import Image
+import gradio as gr
+from pathlib import Path
+
+def load_clip_model(device):
+    model = CLIPModel.from_pretrained("laion/CLIP-ViT-L-14-laion2B-s32B-b82K").to(device)
+    processor = CLIPProcessor.from_pretrained("laion/CLIP-ViT-L-14-laion2B-s32B-b82K")
+    return model, processor
+
+def load_embeddings(embedding_file):
+    df = pd.read_csv(embedding_file)
+    embeddings = df.iloc[:, 1:].values  # Escludi la prima colonna (filename)
+    image_paths = df['filename'].tolist()  # Salva i nomi dei file
+    return embeddings, image_paths
+
+def query_images(text, model, processor, image_embeddings, image_paths, device):
+    # Genera l'embedding per il testo
+    text_inputs = processor(text=[text], return_tensors="pt", padding=True).to(device)
+    with torch.no_grad():
+        text_embedding = model.get_text_features(**text_inputs).cpu().numpy().flatten()
+    
+    # Calcola la similarità coseno tra l'embedding del testo e gli embeddings delle immagini
+    similarities = cosine_similarity([text_embedding], image_embeddings)[0]
+    
+    # Ottieni gli indici delle tre immagini più simili
+    top_indices = similarities.argsort()[-3:][::-1]
+    
+    # Restituisci i percorsi delle immagini più simili e i loro punteggi
+    return [(Path("img") / image_paths[i], similarities[i]) for i in top_indices]
+
+def predict(query_text):
+    similar_images = query_images(query_text, model, processor, embeddings, image_paths, device)
+    image_outputs = []
+    scores = []
+    
+    for img_path, score in similar_images:
+        img = Image.open(img_path)
+        image_outputs.append(img)
+        scores.append(score)
+    
+    # Formatta i punteggi per il DataFrame
+    scores_formatted = [[score] for score in scores]  # Converti in una lista di liste
+    return image_outputs, scores_formatted  # Restituisci le immagini e i punteggi
+
+if __name__ == "__main__":
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model, processor = load_clip_model(device)
+
+    # Carica gli embeddings dal file CSV
+    embedding_file = "embeddings.csv"  # Sostituisci con il percorso corretto
+    embeddings, image_paths = load_embeddings(embedding_file)
+
+    # Crea l'interfaccia Gradio
+    interface = gr.Interface(
+        fn=predict,
+        inputs="text",
+        outputs=[
+            gr.Gallery(label="Similar Images", elem_id="image_gallery"), 
+            gr.Dataframe(label="Similarity Scores", headers=["Score"])  # Rimosso show_footer
+        ],
+        title="Find Similar Images",
+        description="Insert text to find the three most similar images."
+    )
+
+    interface.launch()