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JSON Query Generator V1.0

JSONPath_Generator.py

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+from langchain.prompts import PromptTemplate, SystemMessagePromptTemplate, ChatPromptTemplate, \
+    HumanMessagePromptTemplate
+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
+from langchain.cache import InMemoryCache
+import langchain
+langchain.llm_cache = InMemoryCache()
+import  pandas as pd
+import os
+from langchain.chains import LLMChain
+from langchain.output_parsers import PydanticOutputParser
+from pydantic import BaseModel, Field
+os.environ["OPENAI_API_KEY"] = "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"
+
+
+class JSONPath_Generator:
+    def __init__(self, json_input, target_value, json_condition):
+        self.json_input = json_input
+        self.target_value = target_value
+        self.json_condition = json_condition
+        # os.environ["OPENAI_API_KEY"] = "4b81012d55fb416c9e398f6149c3071e"
+        # self.model = ChatOpenAI()
+        # os.environ["OPENAI_API_TYPE"] = "azure"
+        # os.environ["OPENAI_API_VERSION"] = "2023-03-15-preview"
+        # self.model = AzureChatOpenAI(
+        #     # openaikey=openaikey,
+        #     # openai_api_version="2023-03-15-preview",
+        #     # azure_deployment="text-davinci-003",
+        #     # temperature=0,
+        #     deployment_name="gpt-4",
+        #     model_name="gpt-4",
+        # )
+        self.model = OpenAI(
+            temperature=0,
+            # openai_api_key=self.api_key,
+            model_name="gpt-3.5-turbo-instruct"
+        )
+
+    def create_chat_prompt(self):
+        # System Template
+        with open("system_template.txt", "r") as sys_temp:
+            system_template = sys_temp.read().strip()
+        system_prompt = SystemMessagePromptTemplate.from_template(system_template)
+        # Humman Template
+        with open("human_template.txt", "r") as hum_temp:
+            human_template = hum_temp.read().strip()
+        if self.json_condition != '':
+            human_template += " provided the {json_condition}"
+        human_prompt = HumanMessagePromptTemplate.from_template(human_template)
+        # Chat Prompt
+        self.chat_prompt = ChatPromptTemplate.from_messages([system_prompt, human_prompt])
+
+    def create_llm_chain(self):
+        # self.read_extract_api_details()
+        self.create_chat_prompt()
+        chain = LLMChain(llm=self.model, prompt=self.chat_prompt)
+        self.response = chain.run(Target_value=self.target_value,json_condition = self.json_condition,
+                                  JSON_Input=self.json_input)
+        return self.response

app.py

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+import os
+import streamlit as st
+import tempfile
+import pandas as pd
+from JSONPath_Generator import JSONPath_Generator
+
+#Initialize Streamlit app
+st.set_page_config(page_title="👨‍💻 JSON Path Generator")
+st.header("JSON Path Generator")
+
+#Input Text Area
+json_value = st.text_area(label="JSON Body Input: ")
+target_input = st.text_input(label="Target JSON Key: ")
+json_condition = st.text_input(label="JSON Conditions (If any): ")
+
+if st.button("Submit"):
+    json_path_gen = JSONPath_Generator(json_input=json_value, target_value=target_input,
+                                       json_condition=json_condition)
+    res = json_path_gen.create_llm_chain()
+    st.write(res)

human_template.txt

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+{JSON_Input}
+Generate JSON PATH query to extract the {Target_value} from the above JSON Body

requirements.txt

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+streamlit
+pandas
+langchain
+openai

system_template.txt

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+I want you to act as a Expert in generating JSON Query for the provided JSON and Value need to be fetched.
+I will input the JSON Body and the value need to be fetched, analyze the JSON and return the JSON PATH Query to extract the provided Value from the JSON Body.
+Do not write any explanation or context. Do not output any other answers apart from the JSON PATH QUERY