updated ui

app.py

@@ -1,34 +1,42 @@
 import gradio as gr
-from asr import transcribe_audio
-from llm_agent import get_llm_reply
-from tts import generate_speech
-
-def voice_agent(audio):
-    if audio is None:
-        return "No audio received!", "...", None
-    
-    # ASR
-    user_text = transcribe_audio(audio)
-    
-    # LLM
-    bot_reply = get_llm_reply(user_text)
-    
-    # TTS
-    audio_reply = generate_speech(bot_reply)
-    
-    return user_text, bot_reply, audio_reply
-
-ui = gr.Interface(
-    fn=voice_agent,
-    inputs=gr.Audio(type="filepath", label="🎙️ Upload Voice (.wav or .mp3)"),
-    outputs=[
-        gr.Textbox(label="📝 Transcribed Text"),
-        gr.Textbox(label="🤖 LLM Reply"),
-        gr.Audio(type="filepath", label="🔊 AI Voice Response")
-    ],
-    title="VoiceFreight AI v1.0",
-    description="Upload a voice file. Get a spoken reply from an AI freight agent!"
-)
-
-if __name__ == "__main__":
-    ui.launch()
+from loader import load_and_split_pdf
+from rag_chain import setup_rag_chain
+from tts_engine import generate_voice
+import tempfile
+
+qa_chain = None
+
+def handle_pdf(file):
+    global qa_chain
+    docs = load_and_split_pdf(file.name)
+    qa_chain = setup_rag_chain(docs)
+
+    summary = qa_chain.run("Give me a 1-minute summary as if it’s a podcast intro.")
+    voice_path = generate_voice(summary)
+
+    return summary, voice_path
+
+def ask_question(question):
+    if qa_chain is None:
+        return "Upload a PDF first.", None
+    answer = qa_chain.run(question)
+    voice_path = generate_voice(answer)
+    return answer, voice_path
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🎙️ Notebook LLM Style AI Podcast")
+    with gr.Row():
+        pdf_input = gr.File(label="Upload a PDF", file_types=[".pdf"])
+        summary_out = gr.Textbox(label="📄 Podcast Summary")
+        voice_out = gr.Audio(label="🎧 Audio Summary", type="filepath")
+
+    pdf_input.change(handle_pdf, inputs=pdf_input, outputs=[summary_out, voice_out])
+
+    with gr.Row():
+        user_q = gr.Textbox(label="❓ Ask Something from PDF")
+        bot_reply = gr.Textbox(label="🤖 Answer")
+        audio_reply = gr.Audio(label="🔊 Voice Reply", type="filepath")
+
+    user_q.submit(ask_question, inputs=user_q, outputs=[bot_reply, audio_reply])
+
+demo.launch()