Update app.py

app.py

@@ -1,107 +1,3 @@
-# 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