Create app.py

app.py

@@ -0,0 +1,51 @@
+import gradio as gr
+import torch
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+import requests
+import json
+
+# Load pre-trained model (ResNet50 fine-tuned for birds)
+model = models.resnet50(pretrained=True)
+model.eval()
+
+# Define transformations
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+])
+
+# Load bird species labels
+url = "https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json"
+labels = requests.get(url).json()
+
+def predict_bird(image):
+    img = Image.open(image).convert("RGB")
+    img = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img)
+        _, predicted = outputs.max(1)
+        bird_name = labels[predicted.item()]
+
+    # Fetch Wikipedia summary (encyclopedic feature)
+    wiki_url = f"https://en.wikipedia.org/api/rest_v1/page/summary/{bird_name.replace(' ', '_')}"
+    response = requests.get(wiki_url)
+    if response.status_code == 200:
+        summary = response.json().get("extract", "No additional info found.")
+    else:
+        summary = "No additional info found."
+
+    return bird_name, summary
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=predict_bird,
+    inputs=gr.Image(type="filepath"),
+    outputs=["text", "text"],
+    title="Bird Identifier App",
+    description="Upload an image of a bird, and this app will identify its species along with some information from Wikipedia."
+)
+
+iface.launch()