Create App.py

App.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+import whisper
+from TTS.api import TTS
+
+# 🧠 Lade Sprach-KI Modell (z. B. Mistral)
+model_name = "mistralai/Mistral-7B-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, 
+    device_map="auto", 
+    torch_dtype=torch.float16
+)
+
+# 🎤 Sprach-zu-Text Modell (OpenAI Whisper)
+stt_model = whisper.load_model("base")
+
+# 🗣️ Text-zu-Sprache Modell (deutsche Stimme)
+tts_model = TTS(model_name="tts_models/de/thorsten/tacotron2-DCA", progress_bar=False, gpu=torch.cuda.is_available())
+
+# 🧩 Chat-Verlauf und Antwortgenerierung
+def chat_with_ai(prompt, history=[]):
+    full_prompt = ""
+    for user, bot in history:
+        full_prompt += f"User: {user}\nAssistant: {bot}\n"
+    full_prompt += f"User: {prompt}\nAssistant:"
+    
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.95)
+    reply = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    answer = reply.split("Assistant:")[-1].strip()
+    history.append((prompt, answer))
+    return answer, history
+
+# 🎤 Spracheingabe verarbeiten (Speech to Text)
+def speech_to_text(audio_path):
+    audio = whisper.load_audio(audio_path)
+    audio = whisper.pad_or_trim(audio)
+    mel = whisper.log_mel_spectrogram(audio).to(stt_model.device)
+    result = stt_model.decode(mel)
+    return result.text
+
+# 🗣️ Text in Sprache umwandeln
+def text_to_speech(text):
+    tts_model.tts_to_file(text=text, file_path="tts_output.wav")
+    return "tts_output.wav"
+
+# 🧩 Gradio Oberfläche
+with gr.Blocks(title="Meine KI") as demo:
+    gr.Markdown("## 🤖 Meine eigene KI (deutsch, mit Stimme)")
+    
+    chatbot = gr.Chatbot()
+    text_input = gr.Textbox(label="💬 Nachricht eingeben")
+    audio_input = gr.Audio(source="microphone", type="filepath", label="🎤 Spracheingabe")
+    audio_output = gr.Audio(label="🗣️ KI-Antwort als Audio", type="filepath")
+    state = gr.State([])
+
+    def handle_text(message, history):
+        reply, updated_history = chat_with_ai(message, history)
+        voice = text_to_speech(reply)
+        return reply, updated_history, voice
+
+    def handle_audio(audio, history):
+        transcribed = speech_to_text(audio)
+        reply, updated_history = chat_with_ai(transcribed, history)
+        voice = text_to_speech(reply)
+        return reply, updated_history, voice
+
+    text_input.submit(handle_text, [text_input, state], [chatbot, state, audio_output])
+    audio_input.change(handle_audio, [audio_input, state], [chatbot, state, audio_output])
+
+demo.launch()