import gradio as gr
import os
import faiss
import pickle
import numpy as np
import torch
from PIL import Image
from sentence_transformers import SentenceTransformer
from transformers import CLIPProcessor, CLIPModel
from azure.ai.inference import ChatCompletionsClient
from azure.ai.inference.models import SystemMessage, UserMessage
from azure.core.credentials import AzureKeyCredential

# Load models
text_encoder = SentenceTransformer('all-MiniLM-L6-v2')
clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Embedding functions
def embed_text(text):
    return text_encoder.encode(text)

def embed_image(image_path):
    image = Image.open(image_path).convert("RGB")
    inputs = clip_processor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = clip_model.get_image_features(**inputs)
    return outputs.squeeze().cpu().numpy()

# Search + prompt
def semantic_search_and_prompt(query, top_k=5):
    if isinstance(query, str) and os.path.exists(query):
        query_embedding = embed_image(query).astype('float32').reshape(1, -1)
        index = faiss.read_index("image_vector.index")
        metadata_path = "image_vector.metadata"
    else:
        query_embedding = embed_text(query).astype('float32').reshape(1, -1)
        index = faiss.read_index("text_vector.index")
        metadata_path = "text_vector.metadata"

    with open(metadata_path, "rb") as f:
        metadata = pickle.load(f)

    D, I = index.search(query_embedding, top_k)
    top_k_chunks = [dict(metadata[i], score=float(D[0][j])) for j, i in enumerate(I[0])]

    context = "\n\n".join([
        f"[{chunk['type']} from page {chunk['page']} of {chunk['file']}]:\n{chunk.get('content', '')}"
        for chunk in top_k_chunks
    ])

    if isinstance(query, str) and not os.path.exists(query):
        user_query = query
    else:
        user_query = "What is shown in this image?"

    prompt = f"""
You are an expert assistant helping users answer questions based on a collection of documents.
Use the provided context chunks to answer the question accurately and clearly.

Context: {context}

Question: {user_query}
Answer:"""

    return prompt, top_k_chunks

# Azure LLM setup
endpoint = "https://models.github.ai/inference"
model = "deepseek/DeepSeek-V3-0324"
token = os.getenv("GITHUB_TOKEN")
client = ChatCompletionsClient(endpoint=endpoint, credential=AzureKeyCredential(token))

# Main pipeline for Gradio
def handle_query(text_input, image_input):
    if image_input:
        image_path = "query_image.png"
        image_input.save(image_path)
        query = image_path
    elif text_input:
        query = text_input
    else:
        return "Please provide input", None

    prompt, chunks = semantic_search_and_prompt(query)

    response = client.complete(
        messages=[
            SystemMessage(content="You are a helpful assistant."),
            UserMessage(content=prompt),
        ],
        temperature=1.0,
        top_p=1.0,
        max_tokens=1000,
        model=model
    )

    answer = response.choices[0].message.content
    references = "\n".join([
        f"- **{chunk['file']}** | Page {chunk['page']} | Type: *{chunk['type']}* | Score: `{chunk['score']:.2f}`"
        for chunk in chunks
    ])

    return answer, references

# Gradio UI
def launch_app():
    with gr.Blocks() as demo:
        gr.Markdown("## 📄🎓Multimodal Chatbot for FAST-NUCES")

        with gr.Row():
            text_input = gr.Textbox(label="Enter your query")
            image_input = gr.Image(label="Upload an image", type="pil")

        with gr.Row():
            btn = gr.Button("Submit")
            btn_clear = gr.Button("Clear")

        gr.Markdown("### 🧠 LLM Response:")
        answer_output = gr.Markdown()

        with gr.Accordion("📚 Source References", open=False):
            reference_output = gr.Markdown()

        # Submit button
        btn.click(fn=handle_query,inputs=[text_input, image_input],outputs=[answer_output, reference_output])

        # Clear button
        btn_clear.click(fn=lambda: ("", None, "", ""),inputs=[],outputs=[text_input, image_input, answer_output, reference_output])

    demo.launch()

if __name__ == "__main__":
    launch_app()