Create app.py

app.py

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+from transformers import BlipProcessor, BlipForConditionalGeneration
+from sentence_transformers import SentenceTransformer
+import gradio as gr
+from PIL import Image
+import json
+import torch
+import faiss
+
+# -----------------------------
+# 1. Load BLIP Model
+# -----------------------------
+blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# -----------------------------
+# 2. Load Vector DB (Fish Facts)
+# -----------------------------
+# Sample fish knowledge base
+fish_data = [
+    {"name": "stonefish", "text": "Stonefish is highly venomous and lives in Indo-Pacific coastal waters."},
+    {"name": "pufferfish", "text": "Pufferfish contains tetrodotoxin and is poisonous if not properly prepared."},
+    {"name": "lionfish", "text": "Lionfish has venomous spines and can cause painful stings."},
+    {"name": "tuna", "text": "Tuna is a large, non-poisonous fish consumed globally."},
+    {"name": "salmon", "text": "Salmon is a commonly eaten, non-poisonous fish found in freshwater and ocean habitats."}
+]
+
+texts = [item["text"] for item in fish_data]
+names = [item["name"] for item in fish_data]
+
+# Encode fish data
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+embeddings = embedder.encode(texts)
+
+# Create FAISS index
+dim = embeddings[0].shape[0]
+index = faiss.IndexFlatL2(dim)
+index.add(embeddings)
+
+# Toxic fish list
+toxic_list = ["stonefish", "pufferfish", "lionfish"]
+
+# -----------------------------
+# 3. Fish Identifier Function
+# -----------------------------
+def identify_fish(image):
+    # Step 1: Generate caption from image
+    inputs = blip_processor(image, return_tensors="pt")
+    out = blip_model.generate(**inputs)
+    caption = blip_processor.decode(out[0], skip_special_tokens=True)
+
+    # Step 2: Search fish knowledge base
+    query_embed = embedder.encode([caption])
+    D, I = index.search(query_embed, k=1)
+    matched_fish = fish_data[I[0][0]]
+    name = matched_fish["name"]
+    info = matched_fish["text"]
+
+    # Step 3: Classify toxicity
+    is_poisonous = "Yes 🧪" if name in toxic_list else "No ✅"
+
+    # Step 4: Return result
+    return f"**Image Caption:** {caption}\n\n**Detected Fish:** {name.title()}\n**Poisonous:** {is_poisonous}\n**Fact:** {info}"
+
+# -----------------------------
+# 4. Gradio UI
+# -----------------------------
+demo = gr.Interface(
+    fn=identify_fish,
+    inputs=gr.Image(type="pil"),
+    outputs="markdown",
+    title="🧠 Smart Fish Identifier (BLIP + RAG)",
+    description="Upload a fish image. We use BLIP to describe the fish, then match it with our fish fact knowledge base using RAG to determine if it's poisonous."
+)
+
+if __name__ == '__main__':
+    demo.launch()