Create app.py

app.py

@@ -0,0 +1,196 @@
+#!/usr/bin/env python
+
+from __future__ import annotations
+
+import os
+import string
+
+import gradio as gr
+import PIL.Image
+import torch
+from transformers import AutoProcessor, Blip2ForConditionalGeneration
+import re
+
+DESCRIPTION = "# LLaVA 🌋"
+
+def extract_response_pairs(text):
+    pattern = re.compile(r'(USER:.*?)ASSISTANT:(.*?)(?:$|USER:)', re.DOTALL)
+    matches = pattern.findall(text)
+
+    pairs = [(user.strip(), assistant.strip()) for user, assistant in matches]
+
+    return pairs
+
+
+def postprocess_output(output: str) -> str:
+    if output and output[-1] not in string.punctuation:
+        output += "."
+    return output
+
+
+
+def chat(image, text, temperature, length_penalty,
+         repetition_penalty, max_length, min_length, num_beams, top_p,
+         history_chat):
+  
+  prompt = " ".join(history_chat)
+  prompt = f"USER: <image>\n{text}\nASSISTANT:"
+  
+  outputs = pipe(image, prompt=prompt, 
+                  generate_kwargs={"temperature":temperature,
+                  "length_penalty":length_penalty,
+                  "repetition_penalty":repetition_penalty,
+                  "max_length":max_length,
+                  "min_length":min_length,
+                  "num_beams":num_beams,
+                  "top_p":top_p})
+  
+  output = postprocess_output(outputs[0]["generated_text"])
+  history_chat.append(output)
+  print(f"history_chat is {history_chat}")
+  print(f"user response {user_response}")
+  print(f"assistant response {assistant_response}")
+
+
+  chat_val =  extract_response_pairs(" ".join(history_chat))
+  return chat_val, history_chat
+
+
+css = """
+  #mkd {
+    height: 500px; 
+    overflow: auto; 
+    border: 1px solid #ccc; 
+  }
+  """
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.Markdown("LLaVA is now available in transformers with 4-bit quantization!")
+    chatbot = gr.Chatbot(label="Chat", show_label=False)
+    with gr.Row():
+      image = gr.Image(type="pil")
+      text_input = gr.Text(label="Chat Input", show_label=False, max_lines=1, container=False)
+    
+      
+      
+    history_chat = gr.State(value=[])
+    with gr.Row():
+        clear_chat_button = gr.Button("Clear")
+        chat_button = gr.Button("Submit", variant="primary")
+    with gr.Accordion(label="Advanced settings", open=False):
+        temperature = gr.Slider(
+            label="Temperature",
+            info="Used with nucleus sampling.",
+            minimum=0.5,
+            maximum=1.0,
+            step=0.1,
+            value=1.0,
+        )
+        length_penalty = gr.Slider(
+            label="Length Penalty",
+            info="Set to larger for longer sequence, used with beam search.",
+            minimum=-1.0,
+            maximum=2.0,
+            step=0.2,
+            value=1.0,
+        )
+        repetition_penalty = gr.Slider(
+            label="Repetition Penalty",
+            info="Larger value prevents repetition.",
+            minimum=1.0,
+            maximum=5.0,
+            step=0.5,
+            value=1.5,
+        )
+        max_length = gr.Slider(
+            label="Max Length",
+            minimum=1,
+            maximum=512,
+            step=1,
+            value=50,
+        )
+        min_length = gr.Slider(
+            label="Minimum Length",
+            minimum=1,
+            maximum=100,
+            step=1,
+            value=1,
+        )
+        num_beams = gr.Slider(
+            label="Number of Beams",
+            minimum=1,
+            maximum=10,
+            step=1,
+            value=5,
+        )
+        top_p = gr.Slider(
+            label="Top P",
+            info="Used with nucleus sampling.",
+            minimum=0.5,
+            maximum=1.0,
+            step=0.1,
+            value=0.9,
+        )
+    chat_output = [
+        chatbot,
+        history_chat
+    ]
+    chat_button.click(fn=chat, inputs=[image, 
+            text_input,
+            temperature,
+            length_penalty,
+            repetition_penalty,
+            max_length,
+            min_length,
+            num_beams,
+            top_p,
+            history_chat],
+        outputs=chat_output,
+        api_name="Chat",
+    )
+
+    chat_inputs = [
+        image,
+        text_input,
+        temperature,
+        length_penalty,
+        repetition_penalty,
+        max_length,
+        min_length,
+        num_beams,
+        top_p,
+        history_chat
+    ]
+    text_input.submit(
+        fn=chat,
+        inputs=chat_inputs,
+        outputs=chat_output
+    ).success(
+        fn=lambda: "",
+        outputs=chat_inputs,
+        queue=False,
+        api_name=False,
+    )
+    clear_chat_button.click(
+        fn=lambda: ([], []),
+        inputs=None,
+        outputs=[
+            chatbot,
+            history_chat
+        ],
+        queue=False,
+        api_name="clear",
+    )
+    image.change(
+        fn=lambda: ([], []),
+        inputs=None,
+        outputs=[
+            chatbot,
+            history_chat
+        ],
+        queue=False,
+    )
+    
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch()