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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)
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()
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