Duplicate from microsoft-cognitive-service/mm-react

Co-authored-by: fai ah <[email protected]>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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Dockerfile

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+FROM python:3.10.9
+
+WORKDIR /src
+
+COPY ./MM-REACT /src/MM-REACT
+
+COPY ./requirements.txt /src/requirements.txt
+
+COPY ./langchain-0.0.94-py3-none-any.whl /src/langchain-0.0.94-py3-none-any.whl
+
+RUN pip install --no-cache-dir /src/langchain-0.0.94-py3-none-any.whl
+
+RUN pip install --no-cache-dir --upgrade -r /src/requirements.txt
+
+WORKDIR /src/MM-REACT
+
+
+CMD ["python", "app.py", "--port", "7860", "--openAIModel", "azureChatGPT", "--noIntermediateConv"]

MM-REACT/app.py

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+import re
+import io
+import os
+from typing import Optional, Tuple
+import datetime
+import sys
+import gradio as gr
+import requests
+import json
+from threading import Lock
+from langchain import ConversationChain, LLMChain
+from langchain.agents import load_tools, initialize_agent, Tool
+from langchain.tools.bing_search.tool import BingSearchRun, BingSearchAPIWrapper
+from langchain.chains.conversation.memory import ConversationBufferMemory
+from langchain.llms import OpenAI
+from langchain.chains import PALChain
+from langchain.llms import AzureOpenAI
+from langchain.utilities import ImunAPIWrapper, ImunMultiAPIWrapper
+from openai.error import AuthenticationError, InvalidRequestError, RateLimitError
+import argparse
+import logging
+from opencensus.ext.azure.log_exporter import AzureLogHandler
+import uuid
+
+logger = None
+
+
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+BUG_FOUND_MSG = "Some Functionalities not supported yet. Please refresh and hit 'Click to wake up MM-REACT'"
+AUTH_ERR_MSG = "OpenAI key needed"
+REFRESH_MSG = "Please refresh and hit 'Click to wake up MM-REACT'"
+MAX_TOKENS = 512
+
+
+############## ARGS #################
+AGRS = None
+#####################################
+
+
+def get_logger():
+    global logger
+    if logger is None:
+        logger = logging.getLogger(__name__)
+        logger.addHandler(AzureLogHandler())
+    return logger
+
+
+# load chain
+def load_chain(history, log_state):
+    global ARGS
+
+    if ARGS.openAIModel == 'openAIGPT35':
+        # openAI GPT 3.5
+        llm = OpenAI(temperature=0, max_tokens=MAX_TOKENS)
+    elif ARGS.openAIModel == 'azureChatGPT':
+        # for Azure OpenAI ChatGPT
+        llm = AzureOpenAI(deployment_name="text-chat-davinci-002", model_name="text-chat-davinci-002", temperature=0, max_tokens=MAX_TOKENS)
+    elif ARGS.openAIModel == 'azureGPT35turbo':
+        # for Azure OpenAI gpt3.5 turbo
+        llm = AzureOpenAI(deployment_name="gpt-35-turbo-version-0301", model_name="gpt-35-turbo (version 0301)", temperature=0, max_tokens=MAX_TOKENS)
+    elif ARGS.openAIModel == 'azureTextDavinci003':
+        # for Azure OpenAI text davinci
+        llm = AzureOpenAI(deployment_name="text-davinci-003", model_name="text-davinci-003", temperature=0, max_tokens=MAX_TOKENS)
+
+    memory = ConversationBufferMemory(memory_key="chat_history")
+
+
+    #############################
+    # loading all tools
+
+    imun_dense = ImunAPIWrapper(
+        imun_url="https://ehazarwestus.cognitiveservices.azure.com/computervision/imageanalysis:analyze",
+        params="api-version=2023-02-01-preview&model-version=latest&features=denseCaptions",
+        imun_subscription_key=os.environ.get("IMUN_SUBSCRIPTION_KEY2"))
+
+    imun = ImunAPIWrapper()
+    imun = ImunMultiAPIWrapper(imuns=[imun, imun_dense])
+
+    imun_celeb = ImunAPIWrapper(
+        imun_url="https://cvfiahmed.cognitiveservices.azure.com/vision/v3.2/models/celebrities/analyze",
+        params="")
+
+    imun_read = ImunAPIWrapper(
+        imun_url="https://vigehazar.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-read:analyze",
+        params="api-version=2022-08-31",
+        imun_subscription_key=os.environ.get("IMUN_OCR_SUBSCRIPTION_KEY"))
+
+    imun_receipt = ImunAPIWrapper(
+        imun_url="https://vigehazar.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-receipt:analyze",
+        params="api-version=2022-08-31",
+        imun_subscription_key=os.environ.get("IMUN_OCR_SUBSCRIPTION_KEY"))
+
+    imun_businesscard = ImunAPIWrapper(
+        imun_url="https://vigehazar.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-businessCard:analyze",
+        params="api-version=2022-08-31",
+        imun_subscription_key=os.environ.get("IMUN_OCR_SUBSCRIPTION_KEY"))
+
+    imun_layout = ImunAPIWrapper(
+        imun_url="https://vigehazar.cognitiveservices.azure.com/formrecognizer/documentModels/prebuilt-layout:analyze",
+        params="api-version=2022-08-31",
+        imun_subscription_key=os.environ.get("IMUN_OCR_SUBSCRIPTION_KEY"))
+
+    bing = BingSearchAPIWrapper(k=2)
+
+    def edit_photo(query: str) -> str:
+        endpoint = os.environ.get("PHOTO_EDIT_ENDPOINT_URL")
+        query = query.strip()
+        url_idx = query.rfind(" ")
+        img_url = query[url_idx + 1:].strip()
+        if img_url.endswith((".", "?")):
+            img_url = img_url[:-1]
+        if not img_url.startswith(("http://", "https://")):
+            return "Invalid image URL"
+        img_url = img_url.replace("0.0.0.0", os.environ.get("PHOTO_EDIT_ENDPOINT_URL_SHORT"))
+        instruction = query[:url_idx]
+        # This should be some internal IP to wherever the server runs
+        job = {"image_path": img_url, "instruction": instruction}
+        response = requests.post(endpoint, json=job)
+        if response.status_code != 200:
+            return "Could not finish the task try again later!"
+        return "Here is the edited image " + endpoint + response.json()["edited_image"]
+
+    # these tools should not step on each other's toes
+    tools = [
+        Tool(
+            name="PAL-MATH",
+            func=PALChain.from_math_prompt(llm).run,
+            description=(
+            "A wrapper around calculator. "
+            "A language model that is really good at solving complex word math problems."
+            "Input should be a fully worded hard word math problem."
+            )
+        ),
+        Tool(
+            name = "Image Understanding",
+            func=imun.run,
+            description=(
+            "A wrapper around Image Understanding. "
+            "Useful for when you need to understand what is inside an image (objects, texts, people)."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+        Tool(
+            name = "OCR Understanding",
+            func=imun_read.run,
+            description=(
+            "A wrapper around OCR Understanding (Optical Character Recognition). "
+            "Useful after Image Understanding tool has found text or handwriting is present in the image tags."
+            "This tool can find the actual text, written name, or product name in the image."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+        Tool(
+            name = "Receipt Understanding",
+            func=imun_receipt.run,
+            description=(
+            "A wrapper receipt understanding. "
+            "Useful after Image Understanding tool has recognized a receipt in the image tags."
+            "This tool can find the actual receipt text, prices and detailed items."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+        Tool(
+            name = "Business Card Understanding",
+            func=imun_businesscard.run,
+            description=(
+            "A wrapper around business card understanding. "
+            "Useful after Image Understanding tool has recognized businesscard in the image tags."
+            "This tool can find the actual business card text, name, address, email, website on the card."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+        Tool(
+            name = "Layout Understanding",
+            func=imun_layout.run,
+            description=(
+            "A wrapper around layout and table understanding. "
+            "Useful after Image Understanding tool has recognized businesscard in the image tags."
+            "This tool can find the actual business card text, name, address, email, website on the card."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+        Tool(
+            name = "Celebrity Understanding",
+            func=imun_celeb.run,
+            description=(
+            "A wrapper around celebrity understanding. "
+            "Useful after Image Understanding tool has recognized people in the image tags that could be celebrities."
+            "This tool can find the name of celebrities in the image."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+        BingSearchRun(api_wrapper=bing),
+        Tool(
+            name = "Photo Editing",
+            func=edit_photo,
+            description=(
+            "A wrapper around photo editing. "
+            "Useful to edit an image with a given instruction."
+            "Input should be an image url, or path to an image file (e.g. .jpg, .png)."
+            )
+        ),
+    ]
+
+    chain = initialize_agent(tools, llm, agent="conversational-assistant", verbose=True, memory=memory, return_intermediate_steps=True, max_iterations=4)
+    log_state = log_state or ""
+    print ("log_state {}".format(log_state))
+    log_state = str(uuid.uuid1())
+    print("langchain reloaded")
+    # eproperties = {'custom_dimensions': {'key_1': 'value_1', 'key_2': 'value_2'}} 
+    properties = {'custom_dimensions': {'session': log_state}}
+    get_logger().warning("langchain reloaded", extra=properties)    
+    history = []
+    history.append(("Show me what you got!", "Hi Human, Please upload an image to get started!"))    
+    
+    return history, history, chain, log_state, \
+        gr.Textbox.update(visible=True), \
+        gr.Button.update(visible=True), \
+        gr.UploadButton.update(visible=True), \
+        gr.Row.update(visible=True), \
+        gr.HTML.update(visible=True), \
+        gr.Button.update(variant="secondary")
+
+
+# executes input typed by human
+def run_chain(chain, inp):
+    # global chain
+    
+    output = ""
+    try:
+        output = chain.conversation(input=inp, keep_short=ARGS.noIntermediateConv)
+        # output = chain.run(input=inp)
+    except AuthenticationError as ae:
+        output = AUTH_ERR_MSG + str(datetime.datetime.now()) + ". " + str(ae)
+        print("output", output)
+    except RateLimitError as rle:
+        output = "\n\nRateLimitError: " + str(rle)
+    except ValueError as ve:
+        output = "\n\nValueError: " + str(ve)
+    except InvalidRequestError as ire:
+        output = "\n\nInvalidRequestError: " + str(ire)
+    except Exception as e:
+        output = "\n\n" + BUG_FOUND_MSG + ":\n\n" + str(e)
+
+    return output
+
+# simple chat function wrapper 
+class ChatWrapper:
+
+    def __init__(self):
+        self.lock = Lock()
+
+    def __call__(
+            self, inp: str, history: Optional[Tuple[str, str]], chain: Optional[ConversationChain], log_state
+    ):
+        
+        """Execute the chat functionality."""
+        self.lock.acquire()
+        try:
+            print("\n==== date/time: " + str(datetime.datetime.now()) + " ====")
+            print("inp: " + inp)
+
+            properties = {'custom_dimensions': {'session': log_state}}
+            get_logger().warning("inp: " + inp, extra=properties)
+
+
+            history = history or []
+            # If chain is None, that is because no API key was provided.
+            output = "Please paste your OpenAI key from openai.com to use this app. " + str(datetime.datetime.now())
+            
+            ########################
+            # multi line 
+            outputs = run_chain(chain, inp)
+
+            outputs = process_chain_output(outputs)
+
+            print (" len(outputs) {}".format(len(outputs)))
+            for i, output in enumerate(outputs):
+                if i==0:
+                    history.append((inp, output))    
+                else:
+                    history.append((None, output))
+            
+
+        except Exception as e:
+            raise e
+        finally:
+            self.lock.release()
+
+        print (history)
+        properties = {'custom_dimensions': {'session': log_state}}
+        if outputs is None:
+            outputs = ""
+        get_logger().warning(str(json.dumps(outputs)), extra=properties)
+
+        return history, history, ""
+   
+def add_image_with_path(state, chain, imagepath, log_state):
+    global ARGS
+    state = state or []
+
+    url_input_for_chain = "http://0.0.0.0:{}/file={}".format(ARGS.port, imagepath)
+
+    outputs = run_chain(chain, url_input_for_chain)
+
+    ########################
+    # multi line response handling
+    outputs = process_chain_output(outputs)
+
+    for i, output in enumerate(outputs):
+        if i==0:
+            # state.append((f"![](/file={imagepath})", output))    
+            state.append(((imagepath,), output))
+        else:
+            state.append((None, output))
+    
+
+    print (state)
+    properties = {'custom_dimensions': {'session': log_state}}
+    get_logger().warning("url_input_for_chain: " + url_input_for_chain, extra=properties)
+    if outputs is None:
+        outputs = ""
+    get_logger().warning(str(json.dumps(outputs)), extra=properties)
+    return state, state
+
+
+# upload image 
+def add_image(state, chain, image, log_state):
+    global ARGS
+    state = state or []
+
+    # handling spaces in image path
+    imagepath = image.name.replace(" ", "%20")
+
+    url_input_for_chain = "http://0.0.0.0:{}/file={}".format(ARGS.port, imagepath)
+    
+    outputs = run_chain(chain, url_input_for_chain)
+
+    ########################
+    # multi line response handling
+    outputs = process_chain_output(outputs)
+
+    for i, output in enumerate(outputs):
+        if i==0:
+            state.append(((imagepath,), output))
+        else:
+            state.append((None, output))
+    
+
+    print (state)
+    properties = {'custom_dimensions': {'session': log_state}}
+    get_logger().warning("url_input_for_chain: " + url_input_for_chain, extra=properties)
+    if outputs is None:
+        outputs = ""
+    get_logger().warning(str(json.dumps(outputs)), extra=properties)
+    return state, state
+
+# extract image url from response and process differently
+def replace_with_image_markup(text):
+    img_url = None
+    text= text.strip()
+    url_idx = text.rfind(" ")
+    img_url = text[url_idx + 1:].strip()
+    if img_url.endswith((".", "?")):
+        img_url = img_url[:-1]
+
+    # if img_url is not None:
+    #     img_url = f"![](/file={img_url})"
+    return img_url
+
+# multi line response handling
+def process_chain_output(outputs):
+    global ARGS
+    EMPTY_AI_REPLY = "AI:"
+    # print("outputs {}".format(outputs))
+    if isinstance(outputs, str): # single line output
+        if outputs.strip() == EMPTY_AI_REPLY:
+            outputs = REFRESH_MSG
+        outputs = [outputs]
+    elif isinstance(outputs, list): # multi line output
+        if ARGS.noIntermediateConv: # remove the items with assistant in it. 
+            cleanOutputs = []
+            for output in outputs:
+                if output.strip() == EMPTY_AI_REPLY:
+                    output = REFRESH_MSG
+                # found an edited image url to embed
+                img_url = None
+                # print ("type list: {}".format(output))
+                if "assistant: here is the edited image " in output.lower():
+                    img_url = replace_with_image_markup(output)
+                    cleanOutputs.append("Assistant: Here is the edited image")
+                    if img_url is not None:
+                        cleanOutputs.append((img_url,))
+                else:
+                    cleanOutputs.append(output)
+                # cleanOutputs = cleanOutputs + output+ "."
+            outputs = cleanOutputs
+    
+    return outputs
+
+
+def init_and_kick_off():
+    global ARGS
+    # initalize chatWrapper
+    chat = ChatWrapper()
+
+    exampleTitle = """<h3>Examples to start conversation..</h3>"""    
+    comingSoon = """<center><b><p style="color:Red;">MM-REACT: March 21th version with image understanding capabilities</p></b></center>"""
+
+    with gr.Blocks(css="#tryButton {width: 120px;}") as block:  
+        llm_state = gr.State()
+        history_state = gr.State()
+        chain_state = gr.State()      
+        log_state = gr.State() 
+        
+        reset_btn = gr.Button(value="!!!CLICK to wake up MM-REACT!!!", variant="primary", elem_id="resetbtn").style(full_width=True)
+        gr.HTML(comingSoon)
+
+        example_image_size = 90
+        col_min_width = 80
+        button_variant = "primary"
+        with gr.Row():
+            with gr.Column(scale=1.0, min_width=100):
+                chatbot = gr.Chatbot(elem_id="chatbot", label="MM-REACT Bot").style(height=620)
+            with gr.Column(scale=0.20, min_width=200, visible=False) as exampleCol:
+                with gr.Row():
+                    grExampleTitle = gr.HTML(exampleTitle, visible=False)
+                with gr.Row():
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example3Image = gr.Image("images/receipt.png", interactive=False).style(height=example_image_size, width=example_image_size)
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example3ImageButton = gr.Button(elem_id="tryButton", value="Try it!", variant=button_variant).style(full_width=True)
+                        # dummy text field to hold the path
+                        example3ImagePath = gr.Text("images/receipt.png", interactive=False, visible=False)
+                with gr.Row():
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example1Image = gr.Image("images/money.png", interactive=False).style(height=example_image_size, width=example_image_size)
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example1ImageButton = gr.Button(elem_id="tryButton", value="Try it!", variant=button_variant).style(full_width=True)
+                        # dummy text field to hold the path
+                        example1ImagePath = gr.Text("images/money.png", interactive=False, visible=False)               
+                with gr.Row():
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example2Image = gr.Image("images/cartoon.png", interactive=False).style(height=example_image_size, width=example_image_size)
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example2ImageButton = gr.Button(elem_id="tryButton", value="Try it!", variant=button_variant).style(full_width=True)
+                        # dummy text field to hold the path
+                        example2ImagePath = gr.Text("images/cartoon.png", interactive=False, visible=False)
+                with gr.Row():
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example4Image = gr.Image("images/product.png", interactive=False).style(height=example_image_size, width=example_image_size)
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example4ImageButton = gr.Button(elem_id="tryButton", value="Try it!", variant=button_variant).style(full_width=True)
+                        # dummy text field to hold the path
+                        example4ImagePath = gr.Text("images/product.png", interactive=False, visible=False)
+                with gr.Row():                        
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example5Image = gr.Image("images/celebrity.png", interactive=False).style(height=example_image_size, width=example_image_size)
+                    with gr.Column(scale=0.50, min_width=col_min_width):
+                        example5ImageButton = gr.Button(elem_id="tryButton", value="Try it!", variant=button_variant).style(full_width=True)
+                        # dummy text field to hold the path
+                        example5ImagePath = gr.Text("images/celebrity.png", interactive=False, visible=False)
+
+
+
+        with gr.Row():
+            with gr.Column(scale=0.75):
+                message = gr.Textbox(label="Upload a pic and ask!",
+                                        placeholder="Type your question about the uploaded image",
+                                        lines=1, visible=False)
+            with gr.Column(scale=0.15):
+                submit = gr.Button(value="Send", variant="secondary", visible=False).style(full_width=True)
+            with gr.Column(scale=0.10, min_width=0):
+                btn = gr.UploadButton("🖼️", file_types=["image"], visible=False).style(full_width=True)
+
+
+        message.submit(chat, inputs=[message, history_state, chain_state, log_state], outputs=[chatbot, history_state, message])
+
+        submit.click(chat, inputs=[message, history_state, chain_state, log_state], outputs=[chatbot, history_state, message])
+
+        btn.upload(add_image, inputs=[history_state, chain_state, btn, log_state], outputs=[history_state, chatbot])
+        
+        # load the chain    
+        reset_btn.click(load_chain, inputs=[history_state, log_state], outputs=[chatbot, history_state, chain_state, log_state, message, submit, btn, exampleCol, grExampleTitle, reset_btn])
+
+        # setup listener click for the examples
+        example1ImageButton.click(add_image_with_path, inputs=[history_state, chain_state, example1ImagePath, log_state], outputs=[history_state, chatbot])
+        example2ImageButton.click(add_image_with_path, inputs=[history_state, chain_state, example2ImagePath, log_state], outputs=[history_state, chatbot])
+        example3ImageButton.click(add_image_with_path, inputs=[history_state, chain_state, example3ImagePath, log_state], outputs=[history_state, chatbot])
+        example4ImageButton.click(add_image_with_path, inputs=[history_state, chain_state, example4ImagePath, log_state], outputs=[history_state, chatbot])
+        example5ImageButton.click(add_image_with_path, inputs=[history_state, chain_state, example5ImagePath, log_state], outputs=[history_state, chatbot])
+
+
+    # launch the app
+    block.launch(server_name="0.0.0.0", server_port = ARGS.port)
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--port', type=int, required=False, default=7860)
+    parser.add_argument('--openAIModel', type=str, required=False, default='azureChatGPT')
+    parser.add_argument('--noIntermediateConv', default=False, action='store_true', help='if this flag is turned on no intermediate conversation should be shown')
+
+    global ARGS
+    ARGS = parser.parse_args()
+
+    init_and_kick_off()

README.md

@@ -0,0 +1,12 @@
+---
+title: mm-react
+emoji: 💻
+colorFrom: indigo
+colorTo: pink
+sdk: docker
+pinned: false
+license: other
+duplicated_from: microsoft-cognitive-service/mm-react
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,8 @@
+opencensus==0.11.0
+opencensus-context==0.1.3
+opencensus-ext-azure==1.1.6
+opencensus-ext-logging==0.1.1
+imagesize==1.4.1
+gradio==3.21.0
+openai==0.26.4
+requests==2.28.2