# import gradio as gr
# import requests
# import time
# import json
# import os
# import datetime
# from transformers import AutoModelForCausalLM, AutoTokenizer
# import torch

# ### SET YOUR ASSEMBLYAI API KEY
# ASSEMBLYAI_API_KEY = os.getenv("ASSEMBLYAI_API_KEY", "your_assemblyai_api_key")
# headers = {"authorization": ASSEMBLYAI_API_KEY}
# notes_file = "notes.json"

# ### LOAD LLM
# model_id = "IlmaJiyadh/phi3-4k-ft"
# tokenizer = AutoTokenizer.from_pretrained(model_id)
# model = AutoModelForCausalLM.from_pretrained(
#     model_id,
#     device_map="auto",
#     torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
#     trust_remote_code=True
# )

# ### TRANSCRIBE AUDIO WITH ASSEMBLYAI
# def transcribe(audio_path):
#     with open(audio_path, 'rb') as f:
#         upload_res = requests.post("https://api.assemblyai.com/v2/upload", headers=headers, files={"file": f})
#     audio_url = upload_res.json()["upload_url"]

#     transcript_res = requests.post("https://api.assemblyai.com/v2/transcript", json={"audio_url": audio_url}, headers=headers)
#     transcript_id = transcript_res.json()["id"]

#     while True:
#         poll = requests.get(f"https://api.assemblyai.com/v2/transcript/{transcript_id}", headers=headers).json()
#         if poll['status'] == 'completed':
#             return poll['text']
#         elif poll['status'] == 'error':
#             return f"Transcription failed: {poll['error']}"
#         time.sleep(2)

# ### SUMMARIZE USING LLM
# def summarize(text):
#     prompt = f"Below is a lecture transcript. Take lecture notes in bullet points.\n\nInput:\n{text}\n\nSummary:\n"
#     inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
#     outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7, use_cache=False)
#     return tokenizer.decode(outputs[0], skip_special_tokens=True)

# ## SAVE TO JSON
# def save_note(date, transcript, summary):
#     data = {"date": date, "transcript": transcript, "summary": summary}
#     if os.path.exists(notes_file):
#         with open(notes_file, "r") as f:
#             all_notes = json.load(f)
#     else:
#         all_notes = []
#     all_notes.append(data)
#     with open(notes_file, "w") as f:
#         json.dump(all_notes, f, indent=2)

# ### SEARCH NOTES
# def search_notes(query):
#     if not os.path.exists(notes_file):
#         return "No notes available yet."
#     with open(notes_file, "r") as f:
#         notes = json.load(f)
#     results = [n for n in notes if query.lower() in n['summary'].lower() or query.lower() in n['transcript'].lower()]
#     if not results:
#         return "No matching notes found."
#     return "\n\n".join([f"📅 {n['date']}\n{n['summary']}" for n in results])

# ### FULL PIPELINE
# def full_pipeline(audio):
#     if audio is None:
#         return "No audio provided", "", ""
    
#     transcript = transcribe(audio)
#     summary = summarize(transcript)
#     date_str = str(datetime.date.today())
#     save_note(date_str, transcript, summary)
#     return transcript, summary, f"✅ Lecture saved for {date_str}"

# ### BUILD GRADIO UI
# with gr.Blocks() as demo:
#     gr.Markdown("# 🎓 Lecture Assistant (Audio → Summary + Search)")
#     with gr.Row():
#         with gr.Column():
#             #audio_input = gr.Audio(source="microphone", type="filepath", label="🎙️ Record Audio")
#             audio_input = gr.Audio(type="filepath", label="🎙️ Record Audio")
#             submit_btn = gr.Button("Transcribe & Summarize")
#             transcript_output = gr.Textbox(label="📄 Transcript")
#             summary_output = gr.Textbox(label="📝 Summary")
#             save_status = gr.Textbox(label="💾 Save Status")

#         with gr.Column():
#             search_query = gr.Textbox(label="🔍 Search Notes")
#             search_btn = gr.Button("Search")
#             search_output = gr.Textbox(label="Results")

#     submit_btn.click(fn=full_pipeline, inputs=audio_input, outputs=[transcript_output, summary_output, save_status])
#     search_btn.click(fn=search_notes, inputs=search_query, outputs=search_output)

# demo.launch()

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "IlmaJiyadh/phi3-4k-ft"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    trust_remote_code=True
)

def summarize(text):
    prompt = f"Below is a lecture transcript. Take lecture notes in bullet points.\n\nInput:\n{text}\n\nSummary:\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7, use_cache=False)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

gr.Interface(
    fn=summarize,
    inputs=gr.Textbox(lines=10, label="📄 Paste Transcript"),
    outputs=gr.Textbox(label="📝 Summary"),
    title="🧠 Transcript → Summary (Phi-3 Fine-tuned)",
    description="Test only the summarization step."
).launch()