Create app.py

app.py

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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 a helpful assistant.
+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 into a short, coherent response.
+
+For example:
+Question: "Who is being invoiced?", answer: {"answer": "John Doe", "confidence": 0.98}
+
+You should respond with something like:
+With a high degree of confidence, I can say John Doe is being invoiced.
+
+Question: "What is the invoice total?", answer: [{"answer": "154.08", "confidence": 0.75}, {"answer": "155", "confidence": 0.25}
+
+You should respond with something like:
+I belive the invoice total is $154.08 thought it can also be $155.
+"""}
+
+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("# 🔍 Document Analyzer Chatbot")
+    response = gr.Textbox(lines=5, label="Response")
+    chat = gr.MultimodalTextbox(file_types=["image"], interactive=True,
+                                show_label=False, placeholder="Upload a document image by blicking '+' and ask a question.")
+    chat.submit(chat_fn, inputs=chat, outputs=response)
+
+if __name__ == "__main__":
+    demo.launch()