added app

app.py

@@ -0,0 +1,111 @@
+from PIL import Image
+
+import requests
+import json
+import gradio as gr
+
+
+from io import BytesIO
+
+def encode_image(image):
+    buffered = BytesIO()
+    image.save(buffered, format="JPEG")
+    buffered.seek(0)
+
+    return buffered
+
+
+def query_api(image, prompt, decoding_method):
+    # local host for testing
+    url = "http://34.132.142.70:5000/api/generate"
+
+    data = {"prompt": prompt, "use_nucleus_sampling": decoding_method == "Nucleus sampling"}
+    
+    image = encode_image(image)
+    files = {"image": image}
+
+    response = requests.post(url, data=data, files=files)
+
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return "Error: " + response.text
+
+
+def prepend_question(text):
+    text = text.strip().lower()
+
+    return "question: " + text
+    
+
+def prepend_answer(text):
+    text = text.strip().lower()
+
+    return "answer: " + text
+
+
+def get_prompt_from_history(history):
+    prompts = []
+
+    for i in range(len(history)):
+        if i % 2 == 0:
+            prompts.append(prepend_question(history[i]))
+        else:
+            prompts.append(prepend_answer(history[i]))
+    
+    return "\n".join(prompts)
+
+
+def postp_answer(text):
+    if text.startswith("answer: "):
+        return text[8:]
+    elif text.startswith("a: "):
+        return text[2:]
+    else:
+        return text
+
+
+def prep_question(text):
+    if text.startswith("question: "):
+        text = text[10:]
+    elif text.startswith("q: "):
+        text = text[2:]
+    
+    if not text.endswith("?"):
+        text += "?"
+    
+    return text
+
+
+def inference(image, text_input, decoding_method, history=[]):
+    text_input = prep_question(text_input)
+    history.append(text_input)
+
+    # prompt = '\n'.join(history)
+    prompt = get_prompt_from_history(history)
+    # print("prompt: " + prompt)
+
+    output = query_api(image, prompt, decoding_method)
+    output = [postp_answer(output[0])]
+    history += output
+    
+    chat = [(history[i], history[i+1]) for i in range(0, len(history)-1, 2)]  # convert to tuples of list
+        
+    return chat, history
+
+
+inputs = [gr.inputs.Image(type='pil'),
+          gr.inputs.Textbox(lines=2, label="Text input"),
+          gr.inputs.Radio(choices=['Nucleus sampling','Beam search'], type="value", default="Nucleus sampling", label="Text Decoding Method"),
+          "state",
+         ]
+
+outputs = ["chatbot", "state"]
+           
+title = "BLIP-2"
+description = """Gradio demo for BLIP-2, a multimodal chatbot from Salesforce Research. To use it, simply upload your image, or click one of the examples to load them. Please visit our <a href='https://github.com/salesforce/LAVIS/tree/main/projects/blip2' target='_blank'>project webpage</a>.</p> 
+<p> <strong>Disclaimer</strong>: This is a research prototype and is not intended for production use. No data including but not restricted to text and images is collected. </p>"""
+article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2201.12086' target='_blank'>BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models</a>"
+
+iface = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article)
+iface.launch(enable_queue=True)