import os
import gradio as gr
import whisper
from gtts import gTTS
import io
from groq import Groq
from PyPDF2 import PdfReader
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Set up environment variables
os.environ["GROQ_API_KEY"] = "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"

# Initialize the Groq client
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

# Load the Whisper model
whisper_model = whisper.load_model("base")  # You can choose other models like "small", "medium", "large"

# Initialize the tokenizer and model from the saved checkpoint for RAG
rag_tokenizer = AutoTokenizer.from_pretrained("himmeow/vi-gemma-2b-RAG")
rag_model = AutoModelForCausalLM.from_pretrained(
    "himmeow/vi-gemma-2b-RAG",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Use GPU if available for RAG model
if torch.cuda.is_available():
    rag_model.to("cuda")

# Load PDF content
def load_pdf(pdf_path):
    pdf_text = ""
    with open(pdf_path, "rb") as file:
        reader = PdfReader(file)
        for page_num in range(len(reader.pages)):
            page = reader.pages[page_num]
            text = page.extract_text()
            pdf_text += text + "\n"
    return pdf_text

# Define the prompt format for the RAG model
prompt_template = """
### Instruction and Input:
Based on the following context/document:
{}
Please answer the question: {}

### Response:
{}
"""

# Function to process audio and generate a response using RAG and Groq
def process_audio_rag(file_path):
    try:
        # Load and transcribe the audio using Whisper
        audio = whisper.load_audio(file_path)
        result = whisper_model.transcribe(audio)
        text = result["text"]

        # Load the PDF content (update with your PDF path or pass it as an argument)
        pdf_path = "/content/BN_Cotton.pdf"
        pdf_text = load_pdf(pdf_path)

        # Prepare the input data for the RAG model
        query = text
        input_text = prompt_template.format(pdf_text, query, " ")

        # Encode the input text into input ids for RAG model
        input_ids = rag_tokenizer(input_text, return_tensors="pt")
        if torch.cuda.is_available():
            input_ids = input_ids.to("cuda")

        # Generate text using the RAG model
        outputs = rag_model.generate(
            **input_ids,
            max_new_tokens=500,
            no_repeat_ngram_size=5
        )
        rag_response = rag_tokenizer.decode(outputs[0], skip_special_tokens=True)

        # Generate a response using Groq if needed
        chat_completion = client.chat.completions.create(
            messages=[{"role": "user", "content": rag_response}],
            model="llama3-8b-8192",  # Replace with the correct model if necessary
        )
        response_message = chat_completion.choices[0].message.content.strip()

        # Convert the response text to speech
        tts = gTTS(response_message)
        response_audio_io = io.BytesIO()
        tts.write_to_fp(response_audio_io)
        response_audio_io.seek(0)

        # Save audio to a file to ensure it's generated correctly
        with open("response.mp3", "wb") as audio_file:
            audio_file.write(response_audio_io.getvalue())

        # Return the response text and the path to the saved audio file
        return response_message, "response.mp3"

    except Exception as e:
        return f"An error occurred: {e}", None

# Create a Gradio interface
iface = gr.Interface(
    fn=process_audio_rag,
    inputs=gr.Audio(type="filepath"),
    outputs=[gr.Textbox(label="Response Text"), gr.Audio(label="Response Audio")],
    live=True,
    title="Agriculture Assistant"
)

# Launch the interface with the given title
iface.launch()