import gradio as gr
from huggingface_hub import InferenceClient

import nltk
import json
import io
from fpdf import FPDF
from textblob import TextBlob
import PyPDF2
import tempfile

# Download NLTK punkt tokenizer if needed.
nltk.download("punkt", quiet=True)

###############################################################################
#                           Hugging Face Chat Code                            #
###############################################################################
"""
For more information on Hugging Face Inference API support, please check:
https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""

# Initialize your Hugging Face model client.
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(message, history: list[dict], system_message, max_tokens, temperature, top_p, file_content):
    """
    Calls the model (in non-streaming mode) to get a complete response.
    The file content is appended to the system message as context.
    Expects conversation history in the format: 
      [{"role": "user", "content": ...}, {"role": "assistant", "content": ...}, ...]
    """
    if file_content and file_content.strip():
        system_message += "\n\nFile content:\n" + file_content

    # Build messages list for the API request.
    messages = [{"role": "system", "content": system_message}]
    for entry in history:
        messages.append(entry)
    messages.append({"role": "user", "content": message})
    
    try:
        completion = client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=true,  # Non-streaming mode for simplicity.
            temperature=temperature,
            top_p=top_p,
        )
        response = completion.choices[0].message["content"]
    except Exception as e:
        response = f"Error during model response: {e}"
    return response

###############################################################################
#                       File Upload & Parsing Functions                        #
###############################################################################

def parse_file(file_obj):
    """
    Parses an uploaded file.
    Supports PDF (using PyPDF2) and text files (UTF-8 decoding).
    """
    file_extension = file_obj.name.split('.')[-1].lower()
    if file_extension == "pdf":
        try:
            reader = PyPDF2.PdfReader(file_obj)
            text = ""
            for page in reader.pages:
                text += (page.extract_text() or "") + "\n"
            return text
        except Exception as e:
            return f"Error reading PDF: {e}"
    else:
        try:
            return file_obj.read().decode("utf-8", errors="ignore")
        except Exception as e:
            return f"Error reading file: {e}"

def load_files(files):
    """
    Processes a list of uploaded files (provided as file paths).
    Opens each file, parses its content, and concatenates the text.
    """
    all_text = ""
    for file_path in files:
        try:
            with open(file_path, "rb") as f:
                content = parse_file(f)
            all_text += content + "\n"
        except Exception as e:
            all_text += f"Error processing file {file_path}: {e}\n"
    return all_text

###############################################################################
#                             Gradio UI Layout                                #
###############################################################################

with gr.Blocks() as demo:
    gr.Markdown("# **Combined Chat & File Upload App**")
    gr.Markdown(
        """
This app allows you to upload file(s) (e.g., PDF or TXT) and chat with an AI assistant that uses the uploaded file(s) for context throughout the conversation.

- **Upload File(s):** The file contents are automatically parsed and stored.
- **Chat:** Your message, along with the uploaded file content, is sent to the AI on every prompt.
        """
    )
    
    # State to hold the concatenated file content and conversation history.
    file_content_state = gr.State("")
    chat_history_state = gr.State([])  # List of dictionaries in the form {"role": "user"/"assistant", "content": ...}
    
    # --- File Upload Section ---
    # Use type="filepath" so that we get file paths for processing.
    file_input = gr.File(label="Upload File(s)", file_count="multiple", type="filepath")
    # Automatically process files upon upload.
    file_input.change(fn=load_files, inputs=file_input, outputs=file_content_state)
    
    gr.Markdown("## Chat")
    chatbot = gr.Chatbot(label="Chat History", type="messages")
    user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=2)
    
    # Additional model parameters (adjustable)
    system_prompt = gr.Textbox(label="System Message", value="You are a helpful AI assistant.", interactive=True)
    max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens")
    temperature_slider = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
    top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
    
    def chat_fn(user_msg, history, file_content, system_msg, max_tokens, temperature, top_p):
        if not user_msg.strip():
            return "", history
        # Append user's message (in the required format).
        history.append({"role": "user", "content": user_msg})
        # Get the AI's response.
        response = respond(user_msg, history, system_msg, max_tokens, temperature, top_p, file_content)
        # Append the assistant's response.
        history.append({"role": "assistant", "content": response})
        return "", history
    
    # Trigger sending message on Enter in the textbox.
    user_input.submit(
        fn=chat_fn,
        inputs=[user_input, chat_history_state, file_content_state, system_prompt, max_tokens_slider, temperature_slider, top_p_slider],
        outputs=[user_input, chatbot],
        queue=True
    )
    
    # Also add a "Send" button.
    send_button = gr.Button("Send")
    send_button.click(
        fn=chat_fn,
        inputs=[user_input, chat_history_state, file_content_state, system_prompt, max_tokens_slider, temperature_slider, top_p_slider],
        outputs=[user_input, chatbot],
        queue=True
    )
    
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)

if __name__ == "__main__":
    demo.launch()