Update app.py

app.py

@@ -5,81 +5,96 @@ from sentence_transformers import SentenceTransformer, util
 from deep_translator import GoogleTranslator
 from datasets import load_dataset
 
-print("Downloading dataset and initializing model (this takes a minute on startup)...")
-# Load the dataset directly from Hugging Face
+print("Downloading dataset and initializing model...")
 dataset = load_dataset('JotDe/birds')
 
-# Load our saved embeddings and indices
 print("Loading embeddings...")
 df = pd.read_parquet("bird_embeddings.parquet")
 subset_indices = df['dataset_index'].tolist()
 subset_labels = df['label'].tolist()
 
-# Extract just the vectors (ignoring the index and label columns)
 feature_cols = [c for c in df.columns if c not in ['dataset_index', 'label']]
 embeddings = df[feature_cols].values
 dataset_embeddings = torch.tensor(embeddings, device='cpu')
 
-# Load the AI Models
 model = SentenceTransformer('sentence-transformers/clip-ViT-B-32', device='cpu')
 translator = GoogleTranslator(source='auto', target='en')
 
 def get_recommendations(text_input, top_k=3):
+    if not text_input or text_input.strip() == "":
+        return []
+        
     query_embedding = model.encode(text_input, convert_to_tensor=True, device='cpu')
     similarities = util.cos_sim(query_embedding, dataset_embeddings)[0]
     top_indices = similarities.argsort(descending=True)[:top_k]
     
     recommendations = []
     for idx in top_indices:
-        i = idx.item() # Index inside our subset
-        original_idx = subset_indices[i] # Index inside the real Hugging Face dataset
+        i = idx.item() 
+        # Crucial fix for numpy types!
+        original_idx = int(subset_indices[i])
         
         img = dataset['train'][original_idx]['image']
-        label_id = subset_labels[i]
+        label_id = int(subset_labels[i])
         species_name = dataset['train'].features['label'].int2str(label_id)
-        score = similarities[i].item()
         
         recommendations.append({
             "image": img,
-            "label": species_name,
-            "score": score
+            "label": species_name
         })
     return recommendations
 
 def gradio_interface(text_input):
     if not text_input or text_input.strip() == "":
-        return None, "Please enter a description."
+        return None, "### Please enter a description.", None, "", None, ""
+
     if any(char.isdigit() for char in text_input):
-        return None, "Can't put a number! Please write a bird type (example: Blue Bird)."
+        return None, "### Please put a bird type (example: blue bird)", None, "", None, ""
 
     try:
         english_query = translator.translate(text_input)
-        status_message = f"Translated your search to English: '{english_query}'"
     except:
         english_query = text_input
-        status_message = "Searching in English..."
 
-    results = get_recommendations(text_input=english_query, top_k=10)
+    results = get_recommendations(text_input=english_query, top_k=3)
 
-    if not results:
-        return None, "No matches found."
+    if not results or len(results) < 3:
+        return None, "### No matches found.", None, "", None, ""
 
-    gallery_items = [(r['image'], f"{r['label']} (Score: {r['score']:.2f})") for r in results]
-    return gallery_items, status_message
+    name1 = f"### {results[0]['label']}"
+    name2 = f"### {results[1]['label']}"
+    name3 = f"### {results[2]['label']}"
+
+    return results[0]['image'], name1, results[1]['image'], name2, results[2]['image'], name3
 
 with gr.Blocks(title="🐦 Smart Bird Tracker") as demo:
     gr.Markdown("# 🐦 Smart Bird Tracker")
-    gr.Markdown("Describe the bird you are looking for in **English** or **Spanish**, and the AI will find the closest matches!")
+    gr.Markdown("Describe the bird you are looking for in **English**, **Spanish**, or **Hebrew**, and the AI will find the closest matches!")
+    
+    # The Video Player
     gr.Video(value="video assigment 3.mp4", label="Project Presentation Video")
+
     with gr.Row():
         with gr.Column(scale=1):
             text_in = gr.Textbox(label="Describe the bird", placeholder="Type your description here...")
             submit_btn = gr.Button("Find Birds", variant="primary")
-            output_msg = gr.Textbox(label="Status Message")
 
         with gr.Column(scale=2):
-            gallery = gr.Gallery(label="Top 10 Matches", columns=2, height="auto")
+            with gr.Row():
+                with gr.Column():
+                    out_img1 = gr.Image(label="Top Match 1")
+                    out_name1 = gr.Markdown()
+                with gr.Column():
+                    out_img2 = gr.Image(label="Top Match 2")
+                    out_name2 = gr.Markdown()
+                with gr.Column():
+                    out_img3 = gr.Image(label="Top Match 3")
+                    out_name3 = gr.Markdown()
 
-    submit_btn.click(fn=gradio_interface, inputs=[text_in], outputs=[gallery, output_msg])
+    submit_btn.click(
+        fn=gradio_interface,
+        inputs=[text_in],
+        outputs=[out_img1, out_name1, out_img2, out_name2, out_img3, out_name3]
+    )
 
 demo.launch()