Create app.py

app.py

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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")
+            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()