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 torch
+import requests
+from bs4 import BeautifulSoup
+
+# Load BLIP Model
+blip_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+# Map common fish names to FishBase scientific names
+name_map = {
+    "pufferfish": "Tetraodon",
+    "stonefish": "Synanceia",
+    "lionfish": "Pterois",
+    "tuna": "Thunnus",
+    "salmon": "Salmo-salar",
+    "catfish": "Ictalurus",
+    "tilapia": "Oreochromis"
+}
+
+# Poisonous species (scientific names)
+poisonous_species = ["Tetraodon", "Synanceia", "Pterois"]
+
+# FishBase scraping function
+def get_fishbase_summary(scientific_name):
+    search_url = f"https://www.fishbase.se/summary/{scientific_name}.html"
+    try:
+        response = requests.get(search_url, timeout=10)
+        if response.status_code != 200:
+            return f"FishBase entry not found for: {scientific_name}"
+
+        soup = BeautifulSoup(response.text, "html.parser")
+        summary_section = soup.find("div", {"id": "ssummary"})
+        if summary_section:
+            paragraphs = summary_section.find_all("p")
+            text = "\n\n".join(p.get_text(strip=True) for p in paragraphs if p.get_text(strip=True))
+            return text or f"No summary available for {scientific_name}"
+        else:
+            return f"No detailed summary found for {scientific_name}"
+
+    except Exception as e:
+        return f"Error fetching FishBase data for {scientific_name}: {str(e)}"
+
+# Fish identification 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: Extract fish name from caption
+    fish_name = None
+    for name in name_map:
+        if name in caption.lower():
+            fish_name = name
+            break
+
+    if not fish_name:
+        return f"❌ Could not identify a known fish species in the image caption: '{caption}'"
+
+    # Step 3: Lookup in FishBase
+    scientific_name = name_map[fish_name]
+    summary = get_fishbase_summary(scientific_name)
+
+    # Step 4: Check toxicity
+    is_poisonous = "Yes 🧪" if scientific_name in poisonous_species else "No ✅"
+
+    # Step 5: Final Output
+    return f"**Image Caption:** {caption}\n\n**Detected Fish:** {fish_name.title()}\n**Scientific Name:** {scientific_name}\n**Poisonous:** {is_poisonous}\n\n**📚 FishBase Info:**\n{summary}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=identify_fish,
+    inputs=gr.Image(type="pil"),
+    outputs="markdown",
+    title="🐟 Smart Fish Identifier (BLIP + FishBase)",
+    description="Upload a fish image. We use BLIP to describe the fish, match it with known species, then fetch info from FishBase to check if it's poisonous."
+)
+
+if __name__ == '__main__':
+    demo.launch()