Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import torch
+
+# Gerät auswählen (GPU, falls verfügbar, sonst CPU)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Lade das kleinere Modell
+pipe = pipeline("text-generation", model="google/flan-t5-xxl", device=device)
+
+def chat(message, history):
+  prompt = f"User: {message}\nAssistant:"
+  sequences = pipe(prompt, max_length=256)
+  response = sequences[0]['generated_text'].split("Assistant:")[1].strip()
+  history.append((message, response))
+  return history, history
+
+def transcribe_and_send(audio, history):
+  if audio is None:
+    return history, "Keine Audioaufnahme erhalten."
+
+  try:
+      asr = pipeline("automatic-speech-recognition", model="openai/whisper-base", device=device)
+      transcription = asr(audio)["text"]
+      return chat(transcription, history)
+  except Exception as e:
+      print(f"Fehler bei der Transkription: {e}")
+      return history, "Fehler bei der Audioverarbeitung."
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    state = gr.State([])
+
+    with gr.Row():
+        audio_input = gr.Audio(source="microphone", label="Sprachaufnahme")
+        text_input = gr.Textbox(placeholder="Nachricht eingeben...")
+
+    send_button = gr.Button("Senden (Text)")
+    clear_button = gr.Button("Chat löschen")
+
+    send_button.click(chat, [text_input, state], [state, chatbot])
+    audio_input.stop(transcribe_and_send, [audio_input, state], [state, chatbot])
+    clear_button.click(lambda: [], outputs=[chatbot])
+    text_input.submit(chat, [text_input, state], [state, chatbot])
+
+if __name__ == "__main__":
+    demo.launch()