Create app.py

app.py

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+# app.py
+import os
+import json
+import numpy as np
+from PIL import Image
+import torch
+import torch.nn.functional as F
+from transformers import AutoProcessor, AutoModel
+import faiss
+import gradio as gr
+
+# CONFIG - make sure paths match those produced by build_index.py
+MODEL_ID = "EYEDOL/siglipFULL-agri-finetuned"
+FAISS_DIR = "faiss_data"
+INDEX_FILE = os.path.join(FAISS_DIR, "texts.faiss")
+TEXTS_JSONL = os.path.join(FAISS_DIR, "texts.jsonl")
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+TOP_K = 5
+
+# Load metadata texts into memory
+texts = []
+with open(TEXTS_JSONL, "r", encoding="utf-8") as f:
+    for line in f:
+        obj = json.loads(line.strip())
+        texts.append(obj.get("text", ""))
+
+print(f"Loaded {len(texts)} texts.")
+
+# Load FAISS index
+print("Loading FAISS index...")
+index = faiss.read_index(INDEX_FILE)  # IndexFlatIP saved previously
+# If index is on CPU but you want to use GPU inference in Space, you can move to GPU if available and faiss-gpu installed.
+
+# Load model + processor
+print("Loading model & processor...")
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = AutoModel.from_pretrained(MODEL_ID).to(DEVICE)
+model.eval()
+
+def search_image(image: Image.Image, top_k: int = TOP_K):
+    # Preprocess image
+    inputs = processor(images=image.convert("RGB"), return_tensors="pt").to(DEVICE)
+    with torch.no_grad():
+        img_embed = model.get_image_features(**inputs)  # (1, D)
+        img_embed = img_embed / img_embed.norm(p=2, dim=-1, keepdim=True)
+    img_vec = img_embed.cpu().numpy().astype('float32')  # shape (1, D)
+
+    # Query FAISS (index expects float32)
+    D, I = index.search(img_vec, top_k)  # D=distance matrix (inner product), I=indices
+    results = []
+    for score, idx in zip(D[0], I[0]):
+        if idx < 0:
+            continue
+        text = texts[idx] if idx < len(texts) else ""
+        # score is inner product cosine since vectors were normalized (range -1..1)
+        results.append({"text": text, "score": float(score)})
+    return results
+
+# Build Gradio UI
+def infer_and_format(file, top_k):
+    if file is None:
+        return "Upload an image", None
+    image = Image.open(file).convert("RGB")
+    results = search_image(image, top_k)
+    # build HTML or simple text output
+    lines = []
+    for i, r in enumerate(results, 1):
+        lines.append(f"<b>Rank {i}</b> — score: {r['score']:.4f}<br>{r['text']}")
+    html = "<br><br>".join(lines)
+    return html, image
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Image → Retrieved Texts")
+    with gr.Row():
+        with gr.Column(scale=1):
+            img_in = gr.Image(type="filepath", label="Upload image")
+            k_slider = gr.Slider(1, 10, value=TOP_K, step=1, label="Top K")
+            run_btn = gr.Button("Retrieve")
+        with gr.Column(scale=1):
+            out_html = gr.HTML()
+            out_img = gr.Image(label="Input image (preview)")
+
+    run_btn.click(infer_and_format, inputs=[img_in, k_slider], outputs=[out_html, out_img])
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", 7860)))