Create app.py

app.py

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+import streamlit as st
+import torch
+from PIL import Image
+from transformers import Blip2Processor, Blip2ForConditionalGeneration
+import io
+import time
+
+# Set page config
+st.set_page_config(
+    page_title="🚀 BLIP-2 Caption Generator",
+    page_icon="🚀",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for better styling
+st.markdown("""
+<style>
+    .main-header {
+        text-align: center;
+        padding: 2rem 0;
+        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        border-radius: 10px;
+        margin-bottom: 2rem;
+    }
+    .upload-section {
+        border: 2px dashed #ccc;
+        border-radius: 10px;
+        padding: 2rem;
+        text-align: center;
+        margin: 1rem 0;
+    }
+    .caption-box {
+        background-color: #f0f2f6;
+        border-left: 4px solid #667eea;
+        padding: 1rem;
+        border-radius: 5px;
+        margin: 1rem 0;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+@st.cache_resource
+def load_model():
+    """Load and cache the BLIP-2 model and processor"""
+    try:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        
+        # Use the smaller BLIP-2 model for better performance on Hugging Face Spaces
+        model_name = "Salesforce/blip2-opt-2.7b"
+        
+        processor = Blip2Processor.from_pretrained(model_name)
+        model = Blip2ForConditionalGeneration.from_pretrained(
+            model_name, 
+            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+            device_map="auto" if device == "cuda" else None
+        )
+        
+        if device == "cpu":
+            model = model.to(device)
+        
+        return processor, model, device
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        return None, None, None
+
+def generate_caption(image, processor, model, device, prompt=""):
+    """Generate caption for the uploaded image"""
+    try:
+        # Prepare inputs
+        if prompt:
+            inputs = processor(image, text=prompt, return_tensors="pt").to(device)
+        else:
+            inputs = processor(image, return_tensors="pt").to(device)
+        
+        # Generate caption
+        with torch.no_grad():
+            generated_ids = model.generate(
+                **inputs,
+                max_length=50,
+                num_beams=5,
+                temperature=0.7,
+                do_sample=True,
+                early_stopping=True
+            )
+        
+        # Decode the generated caption
+        caption = processor.decode(generated_ids[0], skip_special_tokens=True)
+        return caption
+    
+    except Exception as e:
+        st.error(f"Error generating caption: {str(e)}")
+        return None
+
+def main():
+    # Header
+    st.markdown("""
+    <div class="main-header">
+        <h1>🚀 BLIP-2 Caption Generator</h1>
+        <p>Upload an image and get AI-generated captions instantly!</p>
+    </div>
+    """, unsafe_allow_html=True)
+    
+    # Sidebar
+    with st.sidebar:
+        st.header("🔧 Settings")
+        st.markdown("### Model Information")
+        st.info("Using **BLIP-2** (Salesforce/blip2-opt-2.7b)")
+        
+        # Custom prompt option
+        custom_prompt = st.text_input(
+            "Custom Prompt (Optional):",
+            placeholder="e.g., 'Question: What is in this image? Answer:'"
+        )
+        
+        st.markdown("### About")
+        st.markdown("""
+        This app uses the **BLIP-2** model to generate natural language descriptions of images.
+        
+        **Features:**
+        - 🖼️ Upload any image format
+        - 🤖 AI-powered captioning  
+        - ⚡ Fast inference
+        - 🎯 Optional custom prompts
+        """)
+    
+    # Main content
+    col1, col2 = st.columns([1, 1])
+    
+    with col1:
+        st.markdown("### 📤 Upload Image")
+        
+        # File uploader
+        uploaded_file = st.file_uploader(
+            "Choose an image file",
+            type=["jpg", "jpeg", "png", "bmp", "tiff"],
+            help="Upload an image to generate a caption"
+        )
+        
+        if uploaded_file is not None:
+            # Display uploaded image
+            image = Image.open(uploaded_file)
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+            
+            # Image info
+            st.markdown(f"""
+            **Image Info:**
+            - Size: {image.size[0]} x {image.size[1]} pixels
+            - Format: {image.format}
+            - Mode: {image.mode}
+            """)
+    
+    with col2:
+        st.markdown("### 🔮 Generated Caption")
+        
+        if uploaded_file is not None:
+            # Load model
+            with st.spinner("Loading BLIP-2 model..."):
+                processor, model, device = load_model()
+            
+            if processor is not None and model is not None:
+                # Generate caption button
+                if st.button("🎯 Generate Caption", type="primary"):
+                    with st.spinner("Generating caption..."):
+                        start_time = time.time()
+                        
+                        # Generate caption
+                        caption = generate_caption(
+                            image, processor, model, device, custom_prompt
+                        )
+                        
+                        end_time = time.time()
+                        
+                        if caption:
+                            # Display caption
+                            st.markdown(f"""
+                            <div class="caption-box">
+                                <h4>📝 Caption:</h4>
+                                <p style="font-size: 16px; font-weight: 500;">{caption}</p>
+                            </div>
+                            """, unsafe_allow_html=True)
+                            
+                            # Performance info
+                            st.success(f"Caption generated in {end_time - start_time:.2f} seconds")
+                            
+                            # Copy to clipboard button
+                            st.code(caption, language=None)
+            else:
+                st.error("Failed to load the model. Please try refreshing the page.")
+        else:
+            st.markdown("""
+            <div class="upload-section">
+                <h3>👆 Upload an image to get started</h3>
+                <p>Supported formats: JPG, PNG, BMP, TIFF</p>
+            </div>
+            """, unsafe_allow_html=True)
+    
+    # Footer
+    st.markdown("---")
+    st.markdown("""
+    <div style="text-align: center; color: #666;">
+        <p>Built using <strong>Streamlit</strong> and <strong>Hugging Face Transformers</strong></p>
+        <p>Powered by <strong>BLIP-2</strong> - Bootstrapping Language-Image Pre-training</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()