NaanhAI project using Gradio as interface.

README.md

@@ -1,6 +1,6 @@
 ---
 title: NaanhAI
-emoji: 🏢
+emoji: 🧠💡
 colorFrom: indigo
 colorTo: green
 sdk: gradio

ailab_crs.py

@@ -0,0 +1,123 @@
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from dotenv import load_dotenv, find_dotenv
+import os, sys, getpass
+from groq import Groq
+import gradio as gr
+
+_ = load_dotenv(find_dotenv())
+groq_api_key = os.environ["GROQ_API_KEY"]
+
+
+class NLP_tasks_crs:
+
+    @classmethod
+    def translator(cls, text: str="Hello world!", language: str="French", style: str="polite"):
+        # call our prompt engineering class
+        prompt = Prompt_engineering_crs(text, language, style)
+        prompt = prompt.translator_prompt()
+        # call our LLM_crs class
+        llm = LLM_crs()
+        llm = llm.chain_llm()
+        result = llm.invoke(prompt)
+        return result
+        
+    @classmethod
+    def summarization(cls, text: str):
+        # call our prompt engineering class
+        prompt = Prompt_engineering_crs(text)
+        prompt = prompt.summarization_prompt()
+        # call our LLM_crs class
+        llm = LLM_crs()
+        llm = llm.chain_llm()
+        result = llm.invoke(prompt)
+        return result
+
+    @classmethod
+    def translator_summarization(cls, text: str, language: str, style: str="polite"):
+        # call our prompt engineering class
+        prompt = Prompt_engineering_crs(text, language, style)
+        prompt = prompt.translate_summarize_prompt()
+        # call our LLM_crs class
+        llm = LLM_crs()
+        llm = llm.chain_llm()
+        result = llm.invoke(prompt)
+        return result
+
+    @classmethod
+    def question_answer(cls, question: str):
+        # call our prompt engineering class
+        prompt = Prompt_engineering_crs()
+        prompt = prompt.question_answer_prompt(question)
+        # call our LLM_crs class
+        llm = LLM_crs()
+        llm = llm.chain_llm()
+        result = llm.invoke(prompt)
+        return result
+
+
+class Prompt_engineering_crs:
+    def __init__(self, text: str="Hello world.", language: str="English", style: str="calm and respectful"):
+        self.text= text
+        self.language= language
+        self.style=style
+
+    def translator_prompt(self):
+        template = """You are the best expert translator of human languagues. Your role is to 
+         firstly detect the correct language of the user text, which is delimited by three 
+         backticks. Secondly, translate that text into the desired language provided by the user, which is 
+         {language}. May sure to use {style} tone as your style. Finally, make sure to sound like a formal native speaker and provide only the final result without 
+         additional information. Thanks.
+         text: ```{text}``` """
+        prompt_template = ChatPromptTemplate.from_template(template)
+        prompt = prompt_template.format_messages(text=self.text, language=self.language, style=self.style)
+        return prompt
+    
+    def summarization_prompt(self):
+        summary = """ You are the best expert summarizer in the world. Your role is to summarize the user text into the detected language. 
+        The provided text is below and between three backticks. Make sure to keep the right context. Don't forget to give
+        a title to the result of the summarization. Finally, make sure to sound like a formal native speaker and provide 
+        only the final result without additional information or comments. Thanks.
+        text: ```{text}```"""
+        prompt_template = ChatPromptTemplate.from_template(summary)
+        prompt = prompt_template.format_messages(text=self.text)
+        return prompt
+    
+    def translate_summarize_prompt(self, language: str="English"):
+        template =""" You are the best translator and summarizer in the world. Your first role is to translate 
+        the below text into {language} language. Indeed, use the below style during the
+        translation. Your second role is to summarize into {language} language, the result of the translation
+        with clear and concise words and expressions. Furthermore, use little imojis during
+        the translation. Finally, make sure to sound like a formal native speaker and provide only the final result without 
+        additional information or comments. Thanks.
+           text: {text} 
+           language: {language}
+           style: {style} """
+        prompt_template = ChatPromptTemplate.from_template(template)
+        prompt = prompt_template.format_messages(text=self.text, language=self.language, style=self.style)
+        return prompt
+    
+    @classmethod
+    def question_answer_prompt(cls, question: str):
+        template = "You are a master of questions and answers. Here, your role is to answer to any questions from " \
+        "from the user. If you do not know any questions, please state that you don't know them. Don't " \
+        "invent answers for the questions you do not know. If you have many answers for a question, provide the accurate" \
+        "ones to the user. Use a polite style to communicate with the user. Indeed, speak like a native speaker. See below the question of the user. " \
+        "question: {question}"
+        prompt_template = ChatPromptTemplate.from_template(template)
+        prompt = prompt_template.format_messages(question = {question})
+        return prompt
+
+
+class LLM_crs:
+    def __init__(self, model="moonshotai/kimi-k2-instruct-0905"):
+        self.model = model
+
+    def chain_llm(self):
+        llm = ChatGroq(model = self.model)
+        parser = StrOutputParser()
+        chain_llm  = llm | parser
+        return chain_llm
+    
+

app.py

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+from ailab_crs import NLP_tasks_crs, Prompt_engineering_crs
+import gradio as gr
+from googletrans import LANGUAGES 
+
+supported_langs = list(LANGUAGES.values())
+
+
+
+def main():
+    nlp_tasks = NLP_tasks_crs()
+
+    with gr.Blocks() as demo:
+        gr.Markdown("# 🧠NaanhAI💡")
+                
+        with gr.Row():
+            with gr.Column():
+                text = gr.Textbox(label="Your Query", lines=8)
+            with gr.Column():
+                with gr.Accordion("Other Parameters For Translation or Summarization Tasks", open= True):
+                    language = gr.Dropdown(choices=supported_langs, label="Select Target Language", value="english")
+                    style = gr.Textbox(label="Choose Your Style", value = "polite")
+                    # style = gr.Dropdown(choices= ["polite", "sad", "happy", "scientific", "religious"], value= "polite")
+        
+        with gr.Row(scale=5):
+            with gr.Column(scale=1, min_width=1):
+                btn = gr.Button("Q&A")
+            with gr.Column(scale=2, min_width=1):
+                btn1 = gr.Button("Translator")
+            with gr.Column(scale=2, min_width=1):
+                btn2 = gr.Button("Summarizer")
+            with gr.Column(scale=2, min_width=1):
+                btn3 = gr.Button("Translator_Summarizer")
+
+        answer = gr.Textbox(label="AI Answer", lines=2)
+
+        btn.click(
+            fn= nlp_tasks.question_answer,
+            inputs= text,
+            outputs=answer
+        )
+        btn1.click(
+            fn= nlp_tasks.translator,
+            inputs= [text, language, style],
+            outputs=answer
+        )
+        btn2.click(
+            fn= nlp_tasks.summarization,
+            inputs= text,
+            outputs=answer
+        )
+        btn3.click(
+            fn= nlp_tasks.translator_summarization,
+            inputs= [text, language, style],
+            outputs=answer
+        )
+
+    demo.launch(share=True)     
+    # demo.launch(mcp_server=True)
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

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+[project]
+name = "NaanhAI"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "googletrans>=4.0.2",
+    "gradio>=6.0.1",
+    "langchain>=1.1.0",
+    "langchain-core>=1.1.0",
+    "langchain-groq>=1.1.0",
+    "python-dotenv>=1.2.1",
+]

requirements.txt

@@ -0,0 +1,6 @@
+langchain-groq
+gradio
+python-dotenv
+langchain
+langchain-core
+googletrans