Create app.py

app.py

@@ -0,0 +1,34 @@
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from PIL import Image
+import torch
+
+# Load LLaVA (Apache-2.0 license, safe to use)
+model_id = "llava-hf/llava-1.5-7b-hf"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForVision2Seq.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    low_cpu_mem_usage=True,
+).to("cuda")
+
+def analyze_chart(image, question="Describe this chart"):
+    # Preprocess
+    inputs = processor(images=image, text=question, return_tensors="pt").to("cuda", torch.float16)
+    
+    # Generate
+    output_ids = model.generate(**inputs, max_new_tokens=200)
+    response = processor.decode(output_ids[0], skip_special_tokens=True)
+    return response
+
+demo = gr.Interface(
+    fn=analyze_chart,
+    inputs=[gr.Image(type="pil"), gr.Textbox(value="Describe this chart")],
+    outputs="text",
+    title="Chart Analyzer (LLaVA)",
+    description="Upload a chart and ask questions like 'What does this chart show?' or 'Which month has highest sales?'."
+)
+
+if __name__ == "__main__":
+    demo.launch()