import os import time import uuid from typing import List, Tuple, Optional, Dict, Union import google.generativeai as genai import gradio as gr from PIL import Image from langchain.chat_models import ChatOpenAI from langchain.prompts import ChatPromptTemplate from langchain.chains import LLMChain, SequentialChain # LangChain function for company analysis def company_analysis(api_key: str, company_name: str) -> dict: os.environ['OPENAI_API_KEY'] = api_key # Set the OpenAI API key as an environment variable llm = ChatOpenAI() ''' # Identify the email's language template1 = "Return the language this email is written in:\n{email}.\nONLY return the language it was written in." prompt1 = ChatPromptTemplate.from_template(template1) chain_1 = LLMChain(llm=llm, prompt=prompt1, output_key="language") # Translate the email to English template2 = "Translate this email from {language} to English. Here is the email:\n" + email prompt2 = ChatPromptTemplate.from_template(template2) chain_2 = LLMChain(llm=llm, prompt=prompt2, output_key="translated_email") # Provide a summary in English template3 = "Create a short summary of this email:\n{translated_email}" prompt3 = ChatPromptTemplate.from_template(template3) chain_3 = LLMChain(llm=llm, prompt=prompt3, output_key="summary") # Provide a reply in English template4 = "Reply to the sender of the email giving a plausible reply based on the {summary} and a promise to address issues" prompt4 = ChatPromptTemplate.from_template(template4) chain_4 = LLMChain(llm=llm, prompt=prompt4, output_key="reply") # Provide a translation back to the original language template5 = "Translate the {reply} back to the original {language} of the email." prompt5 = ChatPromptTemplate.from_template(template5) chain_5 = LLMChain(llm=llm, prompt=prompt5, output_key="translated_reply") seq_chain = SequentialChain(chains=[chain_1, chain_2, chain_3, chain_4, chain_5], input_variables=['email'], output_variables=['language', 'translated_email', 'summary', 'reply', 'translated_reply'], verbose=True) ''' return seq_chain(email) print("google-generativeai:", genai.__version__) GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY") TITLE1 = """

Company Analysis

""" TITLE2 = """

Investment Strategy

""" TITLE3 = """

Profit Prophet

""" SUBTITLE = """

Strategy Agent built with Gemini Pro and Gemini Pro Vision API

""" GETKEY = """
Get an API key GOOGLE API KEY.
""" AVATAR_IMAGES = ( None, "https://media.roboflow.com/spaces/gemini-icon.png" ) movie_script_analysis = "" IMAGE_CACHE_DIRECTORY = "/tmp" IMAGE_WIDTH = 512 CHAT_HISTORY = List[Tuple[Optional[Union[Tuple[str], str]], Optional[str]]] def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]: if not stop_sequences: return None return [sequence.strip() for sequence in stop_sequences.split(",")] def preprocess_image(image: Image.Image) -> Optional[Image.Image]: image_height = int(image.height * IMAGE_WIDTH / image.width) return image.resize((IMAGE_WIDTH, image_height)) def cache_pil_image(image: Image.Image) -> str: image_filename = f"{uuid.uuid4()}.jpeg" os.makedirs(IMAGE_CACHE_DIRECTORY, exist_ok=True) image_path = os.path.join(IMAGE_CACHE_DIRECTORY, image_filename) image.save(image_path, "JPEG") return image_path def preprocess_chat_history( history: CHAT_HISTORY ) -> List[Dict[str, Union[str, List[str]]]]: messages = [] for user_message, model_message in history: if isinstance(user_message, tuple): pass elif user_message is not None: messages.append({'role': 'user', 'parts': [user_message]}) if model_message is not None: messages.append({'role': 'model', 'parts': [model_message]}) return messages def upload(files: Optional[List[str]], chatbot: CHAT_HISTORY) -> CHAT_HISTORY: for file in files: image = Image.open(file).convert('RGB') image = preprocess_image(image) image_path = cache_pil_image(image) chatbot.append(((image_path,), None)) return chatbot def user(text_prompt: str, chatbot: CHAT_HISTORY): if text_prompt: chatbot.append((text_prompt, None)) return "", chatbot def bot( google_key: str, files: Optional[List[str]], temperature: float, max_output_tokens: int, stop_sequences: str, top_k: int, top_p: float, chatbot: CHAT_HISTORY ): if len(chatbot) == 0: return chatbot google_key = google_key if google_key else GOOGLE_API_KEY if not google_key: raise ValueError( "GOOGLE_API_KEY is not set. " "Please follow the instructions in the README to set it up.") genai.configure(api_key=google_key) generation_config = genai.types.GenerationConfig( temperature=temperature, max_output_tokens=max_output_tokens, stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences), top_k=top_k, top_p=top_p) if files: text_prompt = [chatbot[-1][0]] \ if chatbot[-1][0] and isinstance(chatbot[-1][0], str) \ else [] image_prompt = [Image.open(file).convert('RGB') for file in files] model = genai.GenerativeModel('gemini-pro-vision') response = model.generate_content( text_prompt + image_prompt, stream=True, generation_config=generation_config) else: messages = preprocess_chat_history(chatbot) model = genai.GenerativeModel('gemini-pro') response = model.generate_content( messages, stream=True, generation_config=generation_config) # streaming effect chatbot[-1][1] = "" for chunk in response: for i in range(0, len(chunk.text), 10): section = chunk.text[i:i + 10] chatbot[-1][1] += section time.sleep(0.01) yield chatbot google_key_component = gr.Textbox( label="GOOGLE API KEY", value="", type="password", placeholder="...", info="You have to provide your own GOOGLE_API_KEY for this app to function properly", visible=GOOGLE_API_KEY is None ) chatbot_component = gr.Chatbot( label='Gemini Pro Vision', bubble_full_width=False, avatar_images=AVATAR_IMAGES, scale=2, height=400 ) text_prompt_component = gr.Textbox(value=movie_script_analysis, show_label=False, autofocus=True, scale=8, lines=8 ) upload_button_component = gr.UploadButton( label="Upload Images", file_count="multiple", file_types=["image"], scale=1 ) run_button_component = gr.Button(value="Run", variant="primary", scale=1) run_button_analysis = gr.Button(value="Run", variant="primary", scale=1) temperature_component = gr.Slider( minimum=0, maximum=1.0, value=0.4, step=0.05, label="Temperature", info=( "Temperature controls the degree of randomness in token selection. Lower " "temperatures are good for prompts that expect a true or correct response, " "while higher temperatures can lead to more diverse or unexpected results. " )) max_output_tokens_component = gr.Slider( minimum=1, maximum=2048, value=1024, step=1, label="Token limit", info=( "Token limit determines the maximum amount of text output from one prompt. A " "token is approximately four characters. The default value is 2048." )) stop_sequences_component = gr.Textbox( label="Add stop sequence", value="", type="text", placeholder="STOP, END", info=( "A stop sequence is a series of characters (including spaces) that stops " "response generation if the model encounters it. The sequence is not included " "as part of the response. You can add up to five stop sequences." )) top_k_component = gr.Slider( minimum=1, maximum=40, value=32, step=1, label="Top-K", info=( "Top-k changes how the model selects tokens for output. A top-k of 1 means the " "selected token is the most probable among all tokens in the model’s " "vocabulary (also called greedy decoding), while a top-k of 3 means that the " "next token is selected from among the 3 most probable tokens (using " "temperature)." )) top_p_component = gr.Slider( minimum=0, maximum=1, value=1, step=0.01, label="Top-P", info=( "Top-p changes how the model selects tokens for output. Tokens are selected " "from most probable to least until the sum of their probabilities equals the " "top-p value. For example, if tokens A, B, and C have a probability of .3, .2, " "and .1 and the top-p value is .5, then the model will select either A or B as " "the next token (using temperature). " )) user_inputs = [ text_prompt_component, chatbot_component ] bot_inputs = [ google_key_component, upload_button_component, temperature_component, max_output_tokens_component, stop_sequences_component, top_k_component, top_p_component, chatbot_component ] with gr.Blocks() as demo: with gr.Tab("Company Analysis"): gr.HTML(TITLE1) run_button_analysis.click( fn=company_analysis, inputs=[ gr.Textbox(label="Enter your OpenAI API Key:", type="password"), gr.Textbox(label="Enter the Company Name:") ], outputs=[ gr.Textbox(label="Language"), gr.Textbox(label="Summary"), gr.Textbox(label="Translated Email"), gr.Textbox(label="Reply in English"), gr.Textbox(label="Reply in Original Language") ] ) with gr.Tab("Investment Strategy"): gr.HTML(TITLE2) gr.HTML(SUBTITLE) gr.HTML(GETKEY) with gr.Column(): google_key_component.render() chatbot_component.render() with gr.Row(): text_prompt_component.render() upload_button_component.render() run_button_component.render() with gr.Accordion("Parameters", open=False): temperature_component.render() max_output_tokens_component.render() stop_sequences_component.render() with gr.Accordion("Advanced", open=False): top_k_component.render() top_p_component.render() run_button_component.click( fn=user, inputs=user_inputs, outputs=[text_prompt_component, chatbot_component], queue=False ).then( fn=bot, inputs=bot_inputs, outputs=[chatbot_component], ) text_prompt_component.submit( fn=user, inputs=user_inputs, outputs=[text_prompt_component, chatbot_component], queue=False ).then( fn=bot, inputs=bot_inputs, outputs=[chatbot_component], ) upload_button_component.upload( fn=upload, inputs=[upload_button_component, chatbot_component], outputs=[chatbot_component], queue=False ) with gr.Tab("Profit Prophet"): gr.HTML(TITLE3) with gr.Row(): with gr.Column(scale=1): gr.Image(value = "resources/holder.png") with gr.Column(scale=1): gr.Image(value = "resources/holder.png") demo.queue(max_size=99).launch(debug=False, show_error=True)