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# app.py - River Pollution Analyzer with instructblip-flan-t5-xl
import torch
from transformers import (
InstructBlipProcessor,
InstructBlipForConditionalGeneration,
BitsAndBytesConfig
)
import gradio as gr
from PIL import Image
import logging
import functools
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@functools.cache
def get_analyzer():
logger.info("Loading instructblip-flan-t5-xl...")
try:
# 4-bit config (works on GPU if available)
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")
model = InstructBlipForConditionalGeneration.from_pretrained(
"Salesforce/instructblip-flan-t5-xl",
quantization_config=quant_config if torch.cuda.is_available() else None,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="auto"
)
return processor, model
except Exception as e:
logger.error(f"Model load failed: {str(e)}")
raise RuntimeError("Model loading error. Check logs.")
def analyze_image(image):
try:
processor, model = get_analyzer()
prompt = """Analyze river pollution. List pollutants and severity (1-10).
Respond EXACTLY like this:
Pollutants: [list]
Severity: [number]"""
inputs = processor(images=image, text=prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=100,
temperature=0.7
)
result = processor.decode(outputs[0], skip_special_tokens=True)
# Format output
if "Pollutants:" in result and "Severity:" in result:
pollutants = result.split("Pollutants:")[1].split("Severity:")[0].strip()
severity = result.split("Severity:")[1].strip()
return f"""🌊 Analysis Result:
📌 Pollutants: {pollutants}
📈 Severity: {severity}/10"""
return result
except Exception as e:
logger.error(f"Error: {str(e)}")
return f"⚠️ Error (try a smaller image): {str(e)}"
# Minimal UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🌍 River Pollution Analyzer (instructblip-flan-t5-xl)")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload Image")
analyze_btn = gr.Button("Analyze", variant="primary")
output = gr.Textbox(label="Result")
analyze_btn.click(analyze_image, inputs=image_input, outputs=output)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |