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import ast |
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import requests |
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import json |
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import gradio as gr |
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from models import * |
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from pydantic import BaseModel, Field |
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from workflow import app_graph |
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from langchain.output_parsers import PydanticOutputParser |
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from existing_solution import * |
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class RouterResponse_1(BaseModel): |
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route :list[str]= Field(description=("A list of keys relevant to the user's query")) |
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class SummaryResponse_1(BaseModel): |
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information: str=Field(description=("Condensed information based on the context provided")) |
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route_op_1=PydanticOutputParser(pydantic_object=RouterResponse_1) |
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summary_op_1=PydanticOutputParser(pydantic_object=SummaryResponse_1) |
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async def solution_langchain(query,prev_msgs,json_data): |
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response=await router_chain_1.ainvoke({"query":query,"previous_messages":prev_msgs,"format_instructions":route_op_1.get_format_instructions()}) |
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routes=route_op_1.parse(response.content).route |
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print(routes) |
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if len(routes)!=0: |
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result = {key: json_data[key] for key in routes} |
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print(result) |
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response= await summary_chain_1.ainvoke({"query":query,"data":json.dumps(result),"previous_messages":prev_msgs, |
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"format_instructions":summary_op_1.get_format_instructions()}) |
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return summary_op_1.parse(response.content).information |
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else: return "Nothing" |
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async def process_inputs(input_string,uploaded_file): |
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if uploaded_file is not None: |
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try: |
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with open(uploaded_file) as f: |
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file_content = json.load(f) |
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except Exception as e: |
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print(e) |
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else: |
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raise Exception("User data Needed") |
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input_list=[] |
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inputs = {"query": input_string,"previous_msgs":input_list,"ui_data":file_content,'information':[]} |
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extracted_1= await solution_langchain(query=input_string,prev_msgs=input_list,json_data=file_content) |
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final_state= await app_graph.ainvoke(inputs) |
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extracted_2=final_state['information'] |
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print("==="*50) |
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print("LangChain Solution CHATGPT 1\n", extracted_1) |
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print("==="*50) |
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print("LangGraph Solution CHATGPT 2\n", extracted_2) |
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print("==="*50) |
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url = os.getenv("PERSONALITY_URL") + "/chat" |
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message_1 = RESPONSE_PROMPT.format(query=input_string, user_information=extracted_1) |
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payload_1 = { |
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"message": message_1, |
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"personality": 'humanish' |
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} |
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response_1 = requests.post(url, json=payload_1) |
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response_1.raise_for_status() |
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url = os.getenv("PERSONALITY_URL") + "/chat" |
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message_2= RESPONSE_PROMPT.format(query=input_string, user_information=extracted_2) |
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payload_2 = { |
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"message": message_2, |
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"personality": 'humanish' |
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} |
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response_2 = requests.post(url, json=payload_2) |
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response_2.raise_for_status() |
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messages = [ |
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ChatMessage(role="user", content=input_string),] |
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request = ChatRequest( |
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messages=messages, |
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user_preferences=file_content, |
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personality="humanish" |
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) |
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response_3= await chat_endpoint(request) |
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return response_1.json()["response"], response_2.json()["response"], response_3.response |
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interface = gr.Interface( |
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fn=process_inputs, |
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inputs=[ |
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gr.Textbox(label="Enter a string"), |
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gr.File(label="Upload a JSON file", type="filepath") |
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], |
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outputs=[ |
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gr.Textbox(label="Solution 1 Langchain"), |
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gr.Textbox(label="Solution 2 Langgraph"), |
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gr.Textbox(label="Existing Solution"), |
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], |
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title="Extracting Relevant UI", |
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description="Provide a query, previous messages and user_data. Make sure in user data these keys are present :['name', 'age', 'gender', 'preferences', 'personalInformation', 'relatedDemographics', 'history', 'painPoints', 'inefficienciesOrQualityOfLifeImprovements', 'additionalNotes']" |
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) |
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interface.launch() |
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