Update app.py

app.py

@@ -1,98 +1,77 @@
-#!/usr/bin/env python3
-"""
-Hugging Face Spaces App - Vision Model
-Deploy this to HF Spaces for free cloud inference
-"""
-
-import gradio as gr
-from transformers import pipeline
-from PIL import Image
-import io
-
-# Load model once when app starts
-print("Loading vision model...")
-vision_model = pipeline(
-    "image-to-text",
-    model="Salesforce/blip-image-captioning-base",
-    device="cpu"
-)
-print("✓ Model loaded!")
-
-def analyze_image(image, prompt="Describe the image"):
-    """Analyze image with vision model"""
-    if image is None:
-        return "Please upload an image first"
-    
-    try:
-        # Convert to PIL Image if needed
-        if isinstance(image, str):
-            image = Image.open(image)
-        
-        # Resize if too large
-        image.thumbnail((512, 512))
-        
-        # Generate caption
-        result = vision_model(image)
-        caption = result[0]["generated_text"] if result else "No output"
-        
-        return caption
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-# Create Gradio interface
-with gr.Blocks(title="Vision AI - Image Analysis", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-    # 🖼️ Vision AI - Local Image Analysis
-    
-    Analyze images using a lightweight vision model running locally.
-    - **Model**: BLIP Image Captioning (Base)
-    - **Processing**: 100% on device (no cloud required)
-    - **Speed**: Fast inference on CPU
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            gr.Markdown("### Upload Image")
-            image_input = gr.Image(
-                label="Select Image",
-                type="pil",
-                interactive=True
-            )
-            
-        with gr.Column():
-            gr.Markdown("### Settings")
-            prompt_input = gr.Textbox(
-                label="Prompt",
-                value="Describe the image",
-                lines=2
-            )
-    
-    analyze_btn = gr.Button("🔍 Analyze Image", size="lg", variant="primary")
-    
-    gr.Markdown("### Result")
-    output = gr.Textbox(
-        label="Analysis Result",
-        lines=4,
-        interactive=False
-    )
-    
-    # Connect button to function
-    analyze_btn.click(
-        fn=analyze_image,
-        inputs=[image_input, prompt_input],
-        outputs=output
-    )
-    
-    # Example images
-    gr.Examples(
-        examples=[],
-        inputs=[image_input],
-        label="Examples (optional)"
-    )
-
-if __name__ == "__main__":
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True
-    )
+#!/usr/bin/env python3
+"""
+Vision AI - Detailed Image Analysis
+"""
+
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+
+print("Loading vision model...")
+
+# Advanced captioning model for detailed descriptions
+vision_pipeline = pipeline(
+    "image-to-text",
+    model="Salesforce/blip-image-captioning-large",
+    device=-1  # CPU
+)
+
+print("✓ Model loaded!")
+
+def analyze_image_detailed(image, prompt=""):
+    """Analyze image with detailed captioning"""
+    if image is None:
+        return "Please upload an image first"
+    
+    try:
+        if isinstance(image, str):
+            image = Image.open(image)
+        
+        image.thumbnail((512, 512))
+        
+        # Generate detailed caption
+        result = vision_pipeline(image)
+        caption = result[0]["generated_text"] if result else "No output"
+        
+        return caption
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(title="Vision AI - Detailed Analysis", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🖼️ Vision AI - Advanced Image Analysis
+    
+    Get detailed analysis of your images using advanced AI.
+    
+    - **Model**: BLIP Large (Salesforce) 
+    - **Processing**: 100% local (no cloud)
+    - **Analysis**: Comprehensive image descriptions
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Upload Image")
+            image_input = gr.Image(label="Select Image", type="pil")
+            analyze_btn = gr.Button("🔍 Analyze Image", size="lg", variant="primary")
+        
+        with gr.Column():
+            gr.Markdown("### Detailed Analysis")
+            output = gr.Textbox(
+                label="Image Description",
+                lines=8,
+                interactive=False
+            )
+    
+    analyze_btn.click(
+        fn=analyze_image_detailed,
+        inputs=image_input,
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )