import os
import gradio as gr
from langchain.chat_models import ChatOpenAI
from langchain import LLMChain, PromptTemplate
from langchain.memory import ConversationBufferMemory

# ✅ Correct way to fetch the API key
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

# ✅ This MUST be passed explicitly if running outside OpenAI-hosted envs
if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment variables.")

# ✅ Template
template = """You are a helpful assistant to answer user queries and coding questions.
Act like the best programmer and explain everything like explaining it to a 5-year-old.
{chat_history}
User: {user_message}
Chatbot:"""

# ✅ Prompt
prompt = PromptTemplate(
    input_variables=["chat_history", "user_message"], template=template
)

# ✅ Memory
memory = ConversationBufferMemory(memory_key="chat_history")

# ❌ FIXED: temperature must be a float, not a string
llm_chain = LLMChain(
    llm=ChatOpenAI(temperature=0.5, model_name="gpt-3.5-turbo", openai_api_key=OPENAI_API_KEY),
    prompt=prompt,
    verbose=True,
    memory=memory,
)

# ✅ Function needs to return a response AND history (for gr.ChatInterface)
def get_text_response(user_message, history):
    response = llm_chain.predict(user_message=user_message)
    # History must be returned for the Gradio chat interface to maintain context
    return response

# ✅ Create interface
demo = gr.ChatInterface(fn=get_text_response)

# ✅ Required for Hugging Face Spaces (they call this file)
demo.launch()