Initial Commit

api_calls.py

@@ -0,0 +1,43 @@
+import requests
+
+API_ENDPOINT = "http://35.229.175.237:8889/"
+
+# function to call api
+def call_api(api_path, api_params):
+    url = f"{API_ENDPOINT}/{api_path}"
+    response = requests.post(
+        url, json=**api_params, headers={"Content-Type": "application/json"})
+    return response.json()
+
+def api_qa_normal(query, filtered_data, prompt_template):
+    api_path = "qa/normal"
+    api_params = {
+        "query": query,
+        "filtered_data": filtered_data,
+        "prompt_template": prompt_template
+    }
+    return call_api(api_path, api_params)
+
+def api_qa_waterfee(query, filtered_data, prompt_template):
+    api_path = "qa/waterfee"
+    api_params = {
+        "query": query,
+        "filtered_data": filtered_data,
+        "prompt_template": prompt_template
+    }
+    return call_api(api_path, api_params)
+
+def api_ocr(image_filepath, model_provider):
+    api_path = "ocr"
+    api_params = {
+        "image_filepath": image_filepath,
+        "model_provider": model_provider
+    }
+    return call_api(api_path, api_params)
+
+def api_model_cat_pain_assessment(user_input_image):
+    api_path = "model/cat_pain_assessment"
+    api_params = {
+        "user_input_image": user_input_image
+    }
+    return call_api(api_path, api_params)

app.py

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+import arrow
+import gradio as gr
+import os
+import re
+import pandas as pd
+from pathlib import Path
+from time import sleep
+from tqdm import tqdm
+from api_calls import *
+
+ROOT_DIR = Path(__file__).resolve().parents[0]
+
+
+def preview_uploaded_file(file_paths):
+    if file_paths:
+        return gr.update(value=file_paths[0])
+    else:
+        return gr.update(value=None)
+
+def open_data_check(checked):
+    if checked:
+        return gr.update(visible=True)
+    else:
+        return gr.update(visible=False)
+
+def uploaded_file_process(file_path, ocr_model_choice):
+    name, filetype = Path(file_path).parts[-1].split(".")[0], Path(file_path).parts[-1].split(".")[-1]
+    print(name)
+    ocr_extracted_data = api_ocr(
+        image_filepath=file_path, model_provider=ocr_model_choice)
+    return ocr_extracted_data
+
+def reference_from_file(file_paths, ocr_model_choice="Gemini Pro Vision"):
+
+    data_array = []
+    for file_path in tqdm(file_paths):
+        data = uploaded_file_process(file_path, ocr_model_choice=ocr_model_choice)
+        data_array.append(data)
+        sleep(1)
+    return data_array
+
+def print_like_dislike(x: gr.LikeData):
+    print(x.index, x.value, x.liked)
+
+def bot(query, history, data_array, file_paths, qa_prompt_tmpl, checkbox_replace):
+    if data_array:
+        params = {"query": query, "filtered_data": data_array}
+    else:
+        params = {"query": query}
+    if checkbox_replace:
+        params.update({"prompt_template": qa_prompt_tmpl})
+    
+    if not file_paths or "大台北" in file_paths:
+        func = api_qa_waterfee
+    else:
+        func = api_qa_normal
+
+    response = func(**params)
+
+    full_anwser = ""
+    for character in response:
+        full_anwser += character
+        yield full_anwser
+
+def draw_cat_pain_assessment_result(user_input_image):
+    if user_input_image:
+        json_result = api_model_cat_pain_assessment(user_input_image)
+        total_score = sum(list(json_result.values()))
+        df_result = pd.DataFrame(json_result, index=[0]).T.reset_index()
+        df_result.columns = ["a", "b"]
+        return gr.BarPlot(
+            df_result,
+            x="a",
+            y="b",
+            x_title="Aspects",
+            y_title="Score",
+            title="Cat Pain Assessment",
+            vertical=False,
+            height=400,
+            width=800,
+            tooltip=["a", "b"],
+            y_lim=[0, 2],
+            scale=1,
+        ), gr.HTML(
+            '<h3>Total Score</h3>'
+            f'<span style="font-size: 50px;">{total_score}</span>'
+            '<span style="font-size: 40px;">/10</span>'
+        ), gr.HTML(
+            '<h3>Explanation</h3>'
+            '<p>Ear position: 0-2</p>'
+            '<p>Orbital tightening: 0-2</p>'
+            '<p>Muzzle tension: 0-2</p>'
+            '<p>Whiskers change: 0-2</p>'
+            '<p>Head position: 0-2</p>'
+        )
+    else:
+        return gr.update(value=None)
+
+chatbot = gr.Chatbot(
+    [(None, "我是 ESG AI Chat\n有什麼能為您服務的嗎？")],
+    elem_id="chatbot",
+    scale=1,
+    height=700,
+    bubble_full_width=False
+)
+css = """
+#center {text-align: center}
+footer {visibility: hidden}
+a {color: rgb(255, 206, 10) !important}
+"""
+with gr.Blocks(css=css, theme=gr.themes.Monochrome(neutral_hue="lime")) as demo:
+
+    gr.HTML("<h1>GlobalModelAI AI Product Test</h1><p>Made by `GlobalModelAI Abao`</p>", elem_id="center")
+    gr.Markdown("---")
+
+    with gr.Tab("OCR + Text2SQL"):
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("## OCR Processing", elem_id="center")
+                ocr_model_choice = gr.Dropdown(label="Model", value="Gemini Pro Vision", choices=["GPT-4", "Gemini Pro Vision"])
+                file_preview = gr.Image(type="filepath", image_mode="RGB", sources=None, label="File Preview")
+                file_upload = gr.File(label="Upload File", file_types=["png", "jpg", "jpeg", "helc"], file_count='multiple')
+                checkbox_open_data_check = gr.Checkbox(label="Open Data Check")
+                text_data_from_file_check = gr.Textbox(label="File Upload Status", interactive=False, visible=False)
+                gr.Examples(
+                    examples=[
+                        [[f"{ROOT_DIR}/data/image_for_test/screenshot_for_test-esg_report_table.png"]],
+                        [[f"{ROOT_DIR}/data/image_for_test/screenshot_for_test-esg_report_table2.png"],
+                        [f"{ROOT_DIR}/data/image_for_test/screenshot_for_test-esg_report_table3.png"]], 
+                        [[f"{ROOT_DIR}/data/image_for_test/screenshot_for_test-medical_thesis_table.png"],
+                        [f"{ROOT_DIR}/data/image_for_test/screenshot_for_test-medical_thesis_table2.jpg"]]
+                    ],
+                    inputs=file_upload,
+                    outputs=text_data_from_file_check,
+                    fn=reference_from_file,
+                    cache_examples=True,
+                )
+            with gr.Column():
+                gr.Markdown("## Chat with your data", elem_id="center")
+                with gr.Accordion("Revise Your Prompt", open=False):
+                    checkbox_replace = gr.Checkbox(label="Replace with new prompt")
+                    qa_prompt_tmpl = gr.Textbox(
+                        label="希望用於本次問答的prompt",
+                        info="必須使用到的變數：{filtered_data}、{query}",
+                        value=prompt_dict["qa"],
+                        interactive=True,
+                    )
+                
+                chat_interface = gr.ChatInterface(
+                    fn=bot,
+                    additional_inputs=[text_data_from_file_check, file_upload, qa_prompt_tmpl, checkbox_replace],
+                    chatbot=chatbot,
+                )
+                chatbot.like(print_like_dislike, None, None)
+    
+    with gr.Tab("Cat Pain Assessment Model"):
+        gr.Markdown("## Cat Pain Assessment Model", elem_id="center")
+        with gr.Row():
+            user_input_image = gr.Image(
+                type="filepath", image_mode="RGB", 
+                sources=["upload", "webcam", "clipboard"], 
+                label="Upload a cat image")
+            with gr.Column():
+                cat_pain_assessment_barplot = gr.BarPlot(label="Cat Pain Assessment")
+                cat_pain_assessment_score = gr.HTML(elem_id="center")
+                cat_pain_assessment_explanation = gr.HTML()
+        gr.Examples(
+            examples=[
+                [f"{ROOT_DIR}/data/cat_pain_detection/fgs_cat_examples/5f2afc_3c44de4afb8345a2a56828e3dd166f41~mv2.jpg"],
+                [f"{ROOT_DIR}/data/cat_pain_detection/fgs_cat_examples/5f2afc_9d9838561cde41d3b2dc9ef079dc2303~mv2.jpg"],
+                [f"{ROOT_DIR}/data/cat_pain_detection/fgs_cat_examples/5f2afc_da95c2a1a3294701a007d34ec02f62a5~mv2.jpg"],
+            ],
+            inputs=user_input_image,
+            outputs=[cat_pain_assessment_barplot, cat_pain_assessment_score, cat_pain_assessment_explanation],
+            fn=draw_cat_pain_assessment_result,
+            cache_examples=True,
+        )
+
+    # Callbacks
+    ## OCR + Text2SQL
+    file_upload.upload(
+        reference_from_file, [file_upload, ocr_model_choice], [text_data_from_file_check]
+    )
+    file_upload.change(
+        preview_uploaded_file, [file_upload], [file_preview]
+    )
+    ocr_model_choice.change(
+        reference_from_file, [file_upload, ocr_model_choice], [text_data_from_file_check]
+    )
+    checkbox_open_data_check.select(
+        open_data_check, [checkbox_open_data_check], [text_data_from_file_check]
+    )
+
+    ## Cat Pain Assessment Model
+    user_input_image.change(
+        draw_cat_pain_assessment_result, [user_input_image], 
+        [cat_pain_assessment_barplot, cat_pain_assessment_score, cat_pain_assessment_explanation]
+    )
+
+
+if __name__ == "__main__":
+    demo.queue().launch(
+        max_threads=10,
+        server_name=os.environ.get("FRONTEND_HOST", "127.0.0.1"),
+        server_port=int(os.environ.get("FRONTEND_PORT", 7862)),
+    )