Image Captinoning and Segementation Created

app.py

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+import streamlit as st
+import torch
+import numpy as np
+import cv2
+from PIL import Image
+import tempfile
+from torchvision.models.detection import maskrcnn_resnet50_fpn
+from torchvision.transforms import functional as F
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+@st.cache_resource
+def load_models():
+    seg_model = maskrcnn_resnet50_fpn(pretrained=True)
+    seg_model.eval()
+
+    caption_processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+    caption_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+
+    return seg_model, caption_model, caption_processor
+
+seg_model, caption_model, caption_processor = load_models()
+
+st.title("🖼️ Image Segmentation & Captioning App")
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Original Image", use_column_width=True)
+
+    img_np = np.array(image)
+    img_tensor = F.to_tensor(img_np)
+
+    with torch.no_grad():
+        pred = seg_model([img_tensor])[0]
+
+    def apply_masks(img, pred, threshold=0.7):
+        img = img.copy()
+        for i in range(len(pred["boxes"])):
+            score = pred["scores"][i].item()
+            if score < threshold:
+                continue
+            mask = pred["masks"][i, 0].mul(255).byte().cpu().numpy()
+            img[mask > 128] = [0, 255, 0]
+        return img
+
+    masked_img = apply_masks(img_np, pred)
+    st.image(masked_img, caption="Segmented Image", use_column_width=True)
+
+    inputs = caption_processor(images=image, return_tensors="pt")
+    out = caption_model.generate(**inputs)
+    caption = caption_processor.decode(out[0], skip_special_tokens=True)
+    st.markdown(f"**📝 Caption:** _{caption}_")
+
+    result_img = Image.fromarray(masked_img)
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".jpg")
+    result_img.save(temp_file.name)
+
+    with open(temp_file.name, "rb") as f:
+        st.download_button("📥 Download Output", f, file_name="output_result.jpg", mime="image/jpeg")

requirements.txt

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+streamlit
+torch
+torchvision
+transformers
+Pillow
+opencv-python-headless
+timm