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import gradio as gr | |
from huggingface_hub import InferenceClient | |
if gr.NO_RELOAD: | |
client = InferenceClient() | |
system_message = { | |
"role": "system", | |
"content": """ | |
You are an expert in understanding comma separate files or .csv which has records of bank statement with salary and expenses. | |
You will be given a question and a set of answers along with a confidence score between 0 and 1 for each answer. | |
You job is to turn this information from this .csv file into a short, coherent response. | |
For example: | |
Question: "In which category I spent the most ?", answer: {"answer": "Transportation", "confidence": 0.98} | |
You should respond with something like: | |
With a high degree of confidence, I can say Transportation is where you are spending the most money. | |
Question: "How much did I earn in the last year?", answer: [{"answer": "154.08", "confidence": 0.75}, {"answer": "155", "confidence": 0.25} | |
You should respond with something like: | |
You have earned $154.08 in the last year. | |
"""} | |
def chat_fn(multimodal_message): | |
question = multimodal_message["text"] | |
image = multimodal_message["files"][0] | |
answer = client.document_question_answering(image=image, question=question, model="impira/layoutlm-document-qa") | |
answer = [{"answer": a.answer, "confidence": a.score} for a in answer] | |
user_message = {"role": "user", "content": f"Question: {question}, answer: {answer}"} | |
message = "" | |
for token in client.chat_completion(messages=[system_message, user_message], | |
max_tokens=100, | |
stream=True, | |
model="HuggingFaceH4/zephyr-7b-beta"): | |
if token.choices[0].finish_reason is not None: | |
continue | |
message += token.choices[0].delta.content | |
yield message | |
with gr.Blocks() as demo: | |
gr.Markdown("# 🔍 Xray with your recent transitions") | |
response = gr.Textbox(lines=5, label="Response") | |
chat = gr.MultimodalTextbox(file_types=["image"], interactive=True, | |
show_label=False, placeholder="Upload a document image by clicking '+' and ask a question about your records.") | |
chat.submit(chat_fn, inputs=chat, outputs=response) | |
if __name__ == "__main__": | |
demo.launch() | |