Update app.py

app.py

@@ -1,51 +1,45 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from PIL import Image
+from ultralytics import YOLO
 import torch
-import matplotlib.pyplot as plt
-import seaborn as sns
-import numpy as np
-
-# Page setup
-st.set_page_config(page_title="Transflower 🌸", page_icon="🌼", layout="centered")
-
-st.markdown(
-    "<h1 style='text-align: center; color: pink;'>🌸 Transflower 🌸</h1>"
-   
-    unsafe_allow_html=True,
-)
-
-# Load model and tokenizer
-model_name = "t5-small"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
-model.eval()
-
-# Input area
-user_input = st.text_area("🌼 Enter text to summarize or visualize:", height=200)
-
-if st.button("✨ Visualize Transformer Magic ✨"):
-    if not user_input.strip():
-        st.warning("Please enter some text to visualize.")
-    else:
-        # Encode input
-        inputs = tokenizer("summarize: " + user_input, return_tensors="pt", truncation=True)
-
-        # Forward pass manually to get attention
-        with torch.no_grad():
-            encoder_outputs = model.encoder(**inputs, output_attentions=True, return_dict=True)
-            attention = encoder_outputs.attentions[-1][0].mean(dim=0).detach().numpy()
-
-        # Generate summary
-        with torch.no_grad():
-            summary_ids = model.generate(inputs["input_ids"], max_length=50)
-            summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
-
-        st.subheader("🌸 Summary:")
-        st.success(summary)
-
-        st.subheader("💖 Encoder Attention Heatmap:")
-
-        fig, ax = plt.subplots(figsize=(10, 6))
-        sns.heatmap(attention, cmap="YlGnBu", ax=ax)
-        ax.set_title("Encoder Self-Attention Heatmap 💫")
-        st.pyplot(fig)
+
+st.set_page_config(page_title="Animal Detection App", layout="centered")
+
+# Load YOLOv8 model
+@st.cache_resource
+def load_model():
+    return YOLO("yolov8s.pt")
+
+model = load_model()
+
+st.title("🐾 Animal Detection App")
+st.write("Upload an image and let the YOLOv8 model detect animals!")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    with st.spinner("Detecting..."):
+        results = model(image)
+
+        # Display detection results
+       for r in results:
+    rendered_img = r.plot()  # r.plot() gives the image with detections
+    st.image(rendered_img, caption="Detected Image", use_container_width=True)
+
+        result_img = Image.fromarray(results[0].plot()[:, :, ::-1])
+        st.image(result_img, caption="Detected Animals", use_column_width=True)
+
+        # Filter animal predictions
+        animal_labels = ["cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "bird"]
+        names = model.names
+        detections = results[0].boxes.data.cpu().numpy()
+
+        st.subheader("Detections:")
+        for det in detections:
+            class_id = int(det[5])
+            label = names[class_id]
+            if label in animal_labels:
+                st.markdown(f"- **{label}** (Confidence: {det[4]:.2f})")