Create app.py

app.py

@@ -0,0 +1,52 @@
+from transformers import pipeline, BitsAndBytesConfig
+import torch
+
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+# 13B version can be loaded with llava-hf/llava-1.5-13b-hf
+model_id = "llava-hf/llava-1.5-7b-hf"
+
+
+pipe = pipeline("image-to-text", model=model_id, model_kwargs={"quantization_config": quantization_config})
+
+prompt = "USER: <image>\nDescribe this picture\n​ASSISTANT: "
+
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs[0]['generated_text'])
+
+def update_conversation(new_message, history, image):
+
+    if image is None:
+        return "Please upload an image first using the widget on the left"
+
+    conversation_starting_from_image = [[user, assistant] for [user, assistant] in history if not assistant.startswith('Please')]
+
+    prompt = "USER: <image>\n"
+
+    for i in range(len(history)):
+        prompt+=history[i][0]+'\nASSISTANT: '+history[i][1]+"\nUSER: "
+
+    prompt = prompt+new_message+'\nASSISTANT: '
+
+    outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200
+                                                          #, "do_sample" : True,
+                                                          #"temperature" : 0.7
+                                                          })[0]['generated_text']
+
+    return outputs[len(prompt)-6:]
+
+import gradio as gr
+
+with gr.Blocks() as demo:
+
+    with gr.Row():
+      image = gr.Image(type='pil', interactive=True)
+
+      gr.ChatInterface(
+          update_conversation, additional_inputs=[image]
+      )
+
+demo.launch(debug=True)