import streamlit as st
import os
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from PyPDF2 import PdfReader
from peft import get_peft_model, LoraConfig, TaskType

# ✅ Force CPU execution
device = torch.device("cpu")

# 🔹 Load IBM Granite Model (CPU-Compatible)
MODEL_NAME = "ibm-granite/granite-3.1-2b-instruct"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    device_map="cpu",  # Force CPU execution
    torch_dtype=torch.float32  # Use float32 since Hugging Face runs on CPU
)

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

# 🔹 Apply LoRA Fine-Tuning Configuration
lora_config = LoraConfig(
    r=8,
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type=TaskType.CAUSAL_LM
)
model = get_peft_model(model, lora_config)
model.eval()

# 🛠 Function to Read & Extract Text from PDFs
def read_files(file):
    file_context = ""
    reader = PdfReader(file)
    
    for page in reader.pages:
        text = page.extract_text()
        if text:
            file_context += text + "\n"
    
    return file_context.strip()

# 🛠 Function to Format AI Prompts
def format_prompt(system_msg, user_msg, file_context=""):
    if file_context:
        system_msg += f" The user has provided a contract document. Use its context to generate insights, but do not repeat or summarize the document itself."
    return [
        {"role": "system", "content": system_msg},
        {"role": "user", "content": user_msg}
    ]

# 🛠 Function to Generate AI Responses
def generate_response(input_text, max_tokens=1000, top_p=0.9, temperature=0.7):
    model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
    
    with torch.no_grad():
        output = model.generate(
            **model_inputs,
            max_new_tokens=max_tokens,
            do_sample=True,
            top_p=top_p,
            temperature=temperature,
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id
        )
    
    return tokenizer.decode(output[0], skip_special_tokens=True)

# 🛠 Function to Clean AI Output
def post_process(text):
    cleaned = re.sub(r'戥+', '', text)  # Remove unwanted symbols
    lines = cleaned.splitlines()
    unique_lines = list(dict.fromkeys([line.strip() for line in lines if line.strip()]))
    return "\n".join(unique_lines)

# 🛠 Function to Handle RAG with IBM Granite & Streamlit
def granite_simple(prompt, file):
    file_context = read_files(file) if file else ""
    
    system_message = "You are IBM Granite, a legal AI assistant specializing in contract analysis."
    
    messages = format_prompt(system_message, prompt, file_context)
    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    
    response = generate_response(input_text)
    return post_process(response)

# 🔹 Streamlit UI
def main():
    st.set_page_config(page_title="Contract Analysis AI", page_icon="📜")

    st.title("📜 AI-Powered Contract Analysis Tool")
    st.write("Upload a contract document (PDF) for a detailed AI-driven legal and technical analysis.")

    # 🔹 Sidebar Settings
    with st.sidebar:
        st.header("⚙️ Settings")
        max_tokens = st.slider("Max Tokens", 50, 1000, 250, 50)
        top_p = st.slider("Top P (sampling)", 0.1, 1.0, 0.9, 0.1)
        temperature = st.slider("Temperature (creativity)", 0.1, 1.0, 0.7, 0.1)

    # 🔹 File Upload Section
    uploaded_file = st.file_uploader("📂 Upload a contract document (PDF)", type="pdf")

    # ✅ Ensure file upload message is displayed
    if uploaded_file is not None:
        st.session_state["uploaded_file"] = uploaded_file  # Persist file in session state
        st.success("✅ File uploaded successfully!")
        st.write("Click the button below to analyze the contract.")

        # Force button to always render
        st.markdown('<style>div.stButton > button {display: block; width: 100%;}</style>', unsafe_allow_html=True)

        if st.button("🔍 Analyze Document"):
            with st.spinner("Analyzing contract document... ⏳"):
                final_answer = granite_simple(
                    "Perform a detailed technical analysis of the attached contract document, highlighting potential risks, legal pitfalls, compliance issues, and areas where contractual terms may lead to future disputes or operational challenges.",
                    uploaded_file
                )

            # 🔹 Display Analysis Result
            st.subheader("📑 Analysis Result")
            st.write(final_answer)

# 🔥 Run Streamlit App
if __name__ == '__main__':
    main()