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e0c7cec
1
Parent(s):
8582c5e
Update app.py
Browse files
app.py
CHANGED
@@ -2,11 +2,58 @@ import os
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import time
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import uuid
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from typing import List, Tuple, Optional, Dict, Union
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import google.generativeai as genai
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import gradio as gr
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from PIL import Image
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print("google-generativeai:", genai.__version__)
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@@ -165,6 +212,9 @@ upload_button_component = gr.UploadButton(
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label="Upload Images", file_count="multiple", file_types=["image"], scale=1
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)
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run_button_component = gr.Button(value="Run", variant="primary", scale=1)
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temperature_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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@@ -228,6 +278,7 @@ user_inputs = [
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chatbot_component
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]
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bot_inputs = [
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google_key_component,
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upload_button_component,
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@@ -244,6 +295,23 @@ with gr.Blocks() as demo:
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with gr.Tab("Company Analysis"):
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gr.HTML(TITLE1)
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with gr.Tab("Investment Strategy"):
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gr.HTML(TITLE2)
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import time
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import uuid
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from typing import List, Tuple, Optional, Dict, Union
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import google.generativeai as genai
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import gradio as gr
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from PIL import Image
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain.chains import LLMChain, SequentialChain
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# LangChain function for company analysis
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def company_analysis(api_key: str, company_name: str) -> dict:
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os.environ['OPENAI_API_KEY'] = api_key # Set the OpenAI API key as an environment variable
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llm = ChatOpenAI()
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'''
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# Identify the email's language
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template1 = "Return the language this email is written in:\n{email}.\nONLY return the language it was written in."
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prompt1 = ChatPromptTemplate.from_template(template1)
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chain_1 = LLMChain(llm=llm, prompt=prompt1, output_key="language")
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# Translate the email to English
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template2 = "Translate this email from {language} to English. Here is the email:\n" + email
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prompt2 = ChatPromptTemplate.from_template(template2)
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chain_2 = LLMChain(llm=llm, prompt=prompt2, output_key="translated_email")
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# Provide a summary in English
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template3 = "Create a short summary of this email:\n{translated_email}"
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prompt3 = ChatPromptTemplate.from_template(template3)
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chain_3 = LLMChain(llm=llm, prompt=prompt3, output_key="summary")
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# Provide a reply in English
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template4 = "Reply to the sender of the email giving a plausible reply based on the {summary} and a promise to address issues"
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prompt4 = ChatPromptTemplate.from_template(template4)
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chain_4 = LLMChain(llm=llm, prompt=prompt4, output_key="reply")
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# Provide a translation back to the original language
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template5 = "Translate the {reply} back to the original {language} of the email."
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prompt5 = ChatPromptTemplate.from_template(template5)
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chain_5 = LLMChain(llm=llm, prompt=prompt5, output_key="translated_reply")
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seq_chain = SequentialChain(chains=[chain_1, chain_2, chain_3, chain_4, chain_5],
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input_variables=['email'],
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output_variables=['language', 'translated_email', 'summary', 'reply', 'translated_reply'],
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verbose=True)
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'''
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return seq_chain(email)
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print("google-generativeai:", genai.__version__)
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label="Upload Images", file_count="multiple", file_types=["image"], scale=1
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)
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run_button_component = gr.Button(value="Run", variant="primary", scale=1)
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run_button_analysis = gr.Button(value="Run", variant="primary", scale=1)
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temperature_component = gr.Slider(
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minimum=0,
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maximum=1.0,
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chatbot_component
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]
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bot_inputs = [
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google_key_component,
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upload_button_component,
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with gr.Tab("Company Analysis"):
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gr.HTML(TITLE1)
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run_button_analysis.click(
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fn=company_analysis,
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inputs=[
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gr.inputs.Textbox(label="Enter your OpenAI API Key:", type="password"),
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gr.inputs.Textbox(label="Enter the Company Name:")
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],
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outputs=[
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gr.outputs.Textbox(label="Language"),
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gr.outputs.Textbox(label="Summary"),
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gr.outputs.Textbox(label="Translated Email"),
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gr.outputs.Textbox(label="Reply in English"),
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gr.outputs.Textbox(label="Reply in Original Language")
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],
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title="Chain Example: Language, Summary, Translate, Respond & Translate",
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description="Translates an email to English, provides a summary, plausible reply and translates back to the sender"
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)
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with gr.Tab("Investment Strategy"):
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gr.HTML(TITLE2)
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