from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers
import os
import gradio as gr

def GetLlamaResponse(topic):
    llm = CTransformers(
        model_type="llama",
        model="llama-2-7b-chat.ggmlv3.q8_0.bin",
        config={"max_new_tokens": 256, "temperature": 0.4},
    )
    template = """
    Generater a poem for hungry natural who wish to eat a delicious {topic} within 256 words
    """
    prompt = PromptTemplate(
        input_variables=["topic", "word_count", "poem_style", "temperature"],
        template=template,
    )

    response = llm(
        prompt.format(
            word_count=256,
            poem_style='Natural',
            temperature=0.4,
            topic=topic,
        )
    )

    return response


# st.set_page_config(
#     page_title="Generate Poem",
#     page_icon="	:pizza:",
#     layout="centered",
#     initial_sidebar_state="collapsed",
# )

# st.header("Generate poems :pizza:")

# topic = st.text_input("Enter the poem topic")


# col1, col2 = st.columns([10, 10])
# col3 = col3 = st.columns(1)[0]

# with col1:
#     word_count = st.text_input("Enter number of words : ")

# with col2:
#     poem_style = st.selectbox(
#         "Write the poem for", ("Michelin Tasters", "Foodies", "Laymen"), index=2
#     )

# with col3:
#     temperature = st.slider(
#         "Select Temperature", min_value=0.0, max_value=1.0, step=0.01
#     )

# submit = st.button("Generate poem")

# if submit:
#     st.write(GetLlamaResponse(word_count, poem_style, temperature, topic))



inputs_image_url = [
    gr.Textbox(type="text", label="Image URL"),
]

outputs_result_dict = [
    gr.Textbox(type="text", label="Result Dictionary"),
]

interface_image_url = gr.Interface(
    fn=GetLlamaResponse,
    inputs=inputs_image_url,
    outputs=outputs_result_dict,
    title="Dark review detection",
    cache_examples=False,
)

gr.TabbedInterface(
    [interface_image_url],
    tab_names=['Some inference']
).queue().launch()