Add application file

app.py

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+import os
+
+import gradio as gr
+from generate_text import GenerateStyleText
+
+
+def process(model, key, repo, example, prompt):
+    set_key(key)
+    generate_text = GenerateStyleText(example=example, prompt=prompt)
+    if model == "HugginFaceHub":
+        model = repo
+    generate_text.set_imp_llm(model)
+    return generate_text.run()
+
+
+
+def set_key(key):
+    os.environ['OPENAI_API_KEY'] = key
+    os.environ['HUGGINGFACEHUB_API_TOKEN'] = key
+    os.environ['ANTHROPIC_API_KEY'] = key
+
+
+title = "StyleScribble Demo"
+description = "This is a demo of StyleScribble the AI powered app that generates text in your writing style, " \
+              "you can learn more and sign-up for full launch here: https://stylescribble.fly.dev"
+
+model_c = gr.Dropdown(choices=["GPT3", "GPT4", "Claude", "HugginFaceHub"], label="Model", value="GPT3")
+key_c = gr.Textbox(label="API Key")
+repo_c = gr.Textbox(label="HF Repo(if selected HugginFaceHub as model)")
+example_c = gr.Textbox(label="Example Writing:", lines=15)
+prompt_c = gr.Textbox(label="Prompt:", lines=5)
+output = gr.Textbox(label="Generated Text:", lines=39)
+demo = gr.Interface(process, [model_c, key_c, repo_c, example_c, prompt_c], output, title=title, description=description)
+
+demo.launch()

generate_text.py

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+from langchain.base_language import BaseLanguageModel
+from langchain.chains import LLMChain, SequentialChain
+from langchain.chat_models import ChatAnthropic
+from langchain.chat_models import ChatOpenAI
+from langchain.llms import HuggingFaceHub
+from langchain.prompts import (
+    PromptTemplate,
+    ChatPromptTemplate,
+    SystemMessagePromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+
+class GenerateStyleText:
+    example: str
+    prompt: str
+    llm: BaseLanguageModel
+
+    def __init__(self, example=None, prompt=None, llm=None):
+        self.example = example
+        self.prompt = prompt
+        self.llm = llm
+
+    def set_imp_llm(self, model):
+        if model == 'GPT3':
+            self.llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
+        elif model == "GPT4":
+            self.llm = ChatOpenAI(model_name="gpt-4")
+        elif model == "Claude":
+            self.llm = ChatAnthropic()
+        else:
+            self.llm = HuggingFaceHub(repo_id=model)
+
+    def run(self):
+        return self.process()
+
+    def process(self):
+        seq_chain = SequentialChain(
+            chains=[self.get_extract_tone_chain(), self.get_generate_text_chain(self.prompt),
+                    self.get_apply_style_chain()],
+            input_variables=["text"], verbose=True)
+        result = seq_chain({'text': self.example, "style": ""})
+        return str(result.get('result'))
+
+    def create_chain(self, chat_prompt, output_key):
+        return LLMChain(llm=self.llm,
+                        prompt=chat_prompt, output_key=output_key)
+
+    def get_extract_tone_chain(self):
+        template = """Based on the tone and writing style in the seed text, create a style guide for a blog or 
+        publication that captures the essence of the seed’s tone. Emphasize engaging techniques that help readers 
+        feel connected to the content.
+        """
+        system_message_prompt = SystemMessagePromptTemplate.from_template(template)
+        human_template = "{text}"
+        human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
+        chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
+
+        return self.create_chain(chat_prompt, "style")
+
+    def get_generate_text_chain(self, prompt):
+        template = """Generate a text following the user_request(use same language of the request):
+                      {user_request}
+                      """.replace("{user_request}", prompt)
+        return self.create_chain(PromptTemplate.from_template(template),
+                                 "generated_text")
+
+    def get_apply_style_chain(self):
+        template = """STYLE:
+                    {style}
+                    REWRITE THE TEXT BELLOW APPLYING THE STYLE ABOVE(use same language of the request), 
+                    ONLY GENERATE NEW TEXT BASED ON THE STYLE CONTEXT, DO NOT COPY STYLE EXACT PARTS:
+                    {generated_text}
+        """
+
+        prompt = PromptTemplate.from_template(template=template)
+        prompt.partial(style="")
+        return self.create_chain(prompt, "result")

requirements.txt

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+gradio==3.36.1
+langchain==0.0.231