Create app.py

app.py

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+import gradio as gr
+#from transformers import AutoModelForCausalLM, AutoProcessor
+
+# Load the model and processor
+model_id = "microsoft/Phi-3-vision-128k-instruct"
+#model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto", _attn_implementation='flash_attention_2')
+#processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+
+# Define the function to generate text
+def generate_text(image, prompt):
+    # Process the input
+    inputs = ""
+
+    # Generate the text
+    generation_args = {
+        "max_new_tokens": 500,
+        "temperature": 0.0,
+        "do_sample": False,
+    }
+    
+    return image + prompt
+
+# Create the Gradio application
+gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Textbox(label="Prompt")
+    ],
+    outputs=gr.Textbox(),
+    title="Phi-3-Vision Model",
+    description="Generate text based on an image and prompt using the Phi-3-Vision model."
+).launch(share=True)