Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlamaForCausalLM, LlamaTokenizer
+
+# Load the Llama 2 model and processor
+# Note: You'll need to replace these with the actual Llama 3.2 vision model when it becomes available
+model_name = "meta-llama/Llama-2-7b-chat-hf"
+processor = AutoProcessor.from_pretrained(model_name)
+model = LlamaForCausalLM.from_pretrained(model_name)
+tokenizer = LlamaTokenizer.from_pretrained(model_name)
+
+def analyze_construction_image(image):
+    # Process the image
+    inputs = processor(images=image, return_tensors="pt")
+    
+    # Generate text based on the image
+    prompt = "Analyze this construction image and identify the snag category, snag description, and steps to desnag."
+    input_ids = tokenizer(prompt, return_tensors="pt").input_ids
+    
+    # Concatenate the image embeddings with the text input
+    combined_inputs = torch.cat([inputs.pixel_values, input_ids], dim=1)
+    
+    # Generate output
+    outputs = model.generate(combined_inputs, max_length=300)
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Parse the result (this is a simplified example)
+    lines = result.split('\n')
+    snag_category = lines[0] if len(lines) > 0 else "N/A"
+    snag_description = lines[1] if len(lines) > 1 else "N/A"
+    desnag_steps = lines[2:] if len(lines) > 2 else ["N/A"]
+    
+    return snag_category, snag_description, "\n".join(desnag_steps)
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=analyze_construction_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Textbox(label="Snag Category"),
+        gr.Textbox(label="Snag Description"),
+        gr.Textbox(label="Steps to Desnag")
+    ],
+    title="Construction Image Analyzer",
+    description="Upload a construction site image to identify issues and get desnag steps."
+)
+
+# Launch the app
+iface.launch()