Create app.py

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+from transformers import BlipProcessor, BlipForQuestionAnswering
+from PIL import Image
+
+# Load BLIP VQA model
+model_name = "Salesforce/blip-vqa-base"
+processor = BlipProcessor.from_pretrained(model_name)
+model = BlipForQuestionAnswering.from_pretrained(model_name)
+
+def classify_material(image, question):
+    if image is None:
+        return "Please upload an image."
+
+    inputs = processor(image, question, return_tensors="pt")
+    out = model.generate(**inputs)
+    answer = processor.decode(out[0], skip_special_tokens=True)
+    return answer
+
+title = "🧠 Smart Waste Material Identifier"
+description = """
+Upload an image of an object or trash item.  
+Type your question, e.g.:
+- "Ignore the base, what is this made of?"
+- "What type of waste is this?"
+- "Is this plastic, paper, or metal?"
+"""
+
+demo = gr.Interface(
+    fn=classify_material,
+    inputs=[
+        gr.Image(type="pil", label="Upload Trash Image"),
+        gr.Textbox(label="Ask a Question", value="Ignore the base, what is this material?")
+    ],
+    outputs=gr.Textbox(label="AI Answer"),
+    title=title,
+    description=description,
+    examples=[
+        ["example_plastic.jpg", "Ignore background, what material is this?"],
+        ["example_can.jpg", "Is this metal or plastic?"]
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()