Create app.py

app.py

@@ -0,0 +1,60 @@
+from langchain_openai import ChatOpenAI
+import os
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables import chain
+from langchain.prompts.chat import ChatPromptTemplate
+from langchain_core.pydantic_v1 import BaseModel, Field
+from langchain_core.output_parsers import JsonOutputParser
+
+os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
+
+email_prompt = """
+  You are an email writer. Use the following input to draft an email:
+
+  Input: {input}
+
+  Deliver:
+  1. A complete email.
+"""
+
+class Email(BaseModel):
+  email: str = Field(description= "email")
+email_parser = JsonOutputParser(pydantic_object=Email)
+
+@chain
+def email_model(inputs: dict) -> str | list[str] | dict:
+ model = ChatOpenAI(temperature=0.5, model="gpt-4o", max_tokens=1024)
+ msg = model.invoke(
+             [HumanMessage(
+             content=[
+             {"type": "text", "text": inputs["prompt"]},
+             {"type": "text", "text": inputs["parser"].get_format_instructions()},
+             ])]
+             )
+ return msg.content
+
+
+def get_email(user_input) -> dict:
+   parser = email_parser
+   prompt = email_prompt.format(input=user_input)
+   intent_chain = email_model | parser
+   return intent_chain.invoke({'prompt': prompt, 'parser':parser})
+
+
+import gradio as gr
+
+def process_text(input_text):
+    output =  get_email(input_text)
+    return output["email"]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=process_text,           # Function to process the text
+    inputs=gr.Textbox(label = "Email Instructions"),       # Textbox input for the user
+    outputs=gr.Textbox(label = "Email"),      # Textbox output for the response
+    title="Email Writer",    # Title of the app
+    # description="Enter email instructions"
+)
+
+# Launch the app
+interface.launch()