Update app.py

app.py

@@ -78,55 +78,144 @@ print("DiffusionPipeline Loaded!")
 model_audio_gen = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small").to("cuda")
 processor_audio_gen = AutoProcessor.from_pretrained("facebook/musicgen-small")
 
+##### Chat z LLAMA ####
+##### Chat z LLAMA ####
+##### Chat z LLAMA ####
+
+
+def _load_model_tokenizer():
+    model_id = 'tangger/Qwen-7B-Chat'
+    tokenizer = AutoTokenizer.from_pretrained(model_id,  trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto",trust_remote_code=True, fp16=True).eval()  
+    return model, tokenizer
+model, tokenizer = _load_model_tokenizer()
+
+
+def postprocess(self, y):
+    if y is None:
+        return []
+    for i, (message, response) in enumerate(y):
+        y[i] = (
+            None if message is None else mdtex2html.convert(message),
+            None if response is None else mdtex2html.convert(response),
+        )
+    return y
+
+
+def _parse_text(text):
+    lines = text.split("\n")
+    lines = [line for line in lines if line != ""]
+    count = 0
+    for i, line in enumerate(lines):
+        if "```" in line:
+            count += 1
+            items = line.split("`")
+            if count % 2 == 1:
+                lines[i] = f'<pre><code class="language-{items[-1]}">'
+            else:
+                lines[i] = f"<br></code></pre>"
+        else:
+            if i > 0:
+                if count % 2 == 1:
+                    line = line.replace("`", r"\`")
+                    line = line.replace("<", "&lt;")
+                    line = line.replace(">", "&gt;")
+                    line = line.replace(" ", "&nbsp;")
+                    line = line.replace("*", "&ast;")
+                    line = line.replace("_", "&lowbar;")
+                    line = line.replace("-", "&#45;")
+                    line = line.replace(".", "&#46;")
+                    line = line.replace("!", "&#33;")
+                    line = line.replace("(", "&#40;")
+                    line = line.replace(")", "&#41;")
+                    line = line.replace("$", "&#36;")
+                lines[i] = "<br>" + line
+    text = "".join(lines)
+    return text
+
+def predict(_query, _chatbot, _task_history):
+    print(f"User: {_parse_text(_query)}")
+    _chatbot.append((_parse_text(_query), ""))
+    full_response = ""
+
+    for response in model.chat_stream(tokenizer, _query, history=_task_history,system = "Jesteś assystentem AI. Odpowiadaj zawsze w języku poslkim"  ):
+        _chatbot[-1] = (_parse_text(_query), _parse_text(response))
+
+        yield _chatbot
+        full_response = _parse_text(response)
+
+    print(f"History: {_task_history}")
+    _task_history.append((_query, full_response))
+    print(f"Qwen-7B-Chat: {_parse_text(full_response)}")
+
+def read_text(text):
+    print("___Tekst do przeczytania!")
+    inputs = tokenizer_tss(text, return_tensors="pt").to("cuda")
+    with torch.no_grad():
+        output = tts_model(**inputs).waveform.squeeze().cpu().numpy()
+    sf.write('temp_file.wav', output, tts_model.config.sampling_rate)
+    return 'temp_file.wav'
+
+
+def update_audio(text):
+    return 'temp_file.wav'
+
+def translate(audio):
+    print("__Wysyłam nagranie do whisper!")
+    transcription = whisper_model.transcribe(audio, language="pl")
+    return transcription["text"]
+
+
+def predict(audio, _chatbot, _task_history):
+    # Użyj funkcji translate, aby przekształcić audio w tekst
+    _query = translate(audio)
+
+    print(f"____User: {_parse_text(_query)}")
+    _chatbot.append((_parse_text(_query), ""))
+    full_response = "" 
+
+    for response in model.chat_stream(tokenizer,
+                                      _query,
+                                      history= _task_history,
+                                      system = "Jesteś assystentem AI. Odpowiadaj zawsze w języku polskim. Odpowiadaj krótko."):
+        _chatbot[-1] = (_parse_text(_query), _parse_text(response))
+        yield _chatbot
+        full_response = _parse_text(response)
+
+    print(f"____History: {_task_history}")
+    _task_history.append((_query, full_response))
+    print(f"__Qwen-7B-Chat: {_parse_text(full_response)}")
+    print("____full_response",full_response)
+    audio_file = read_text(_parse_text(full_response))  # Generowanie audio
+    return full_response
+    # return 'temp_file.wav'  # Zwrócenie ścieżki do pliku audio
+
+def regenerate(_chatbot, _task_history):
+    if not _task_history:
+        yield _chatbot
+        return
+    item = _task_history.pop(-1)
+    _chatbot.pop(-1)
+    yield from predict(item[0], _chatbot, _task_history)
+
 with gr.Blocks() as chat_demo:
-    chatbot = gr.Chatbot()
-    audio_input = gr.Audio(source="microphone", type="filepath", show_label=False)
-    submit_audio = gr.Button("Submit Audio")
-    clear = gr.Button("Clear")
+    chatbot = gr.Chatbot(label='Llama Voice Chatbot', elem_classes="control-height")
+    query = gr.Textbox(lines=2, label='Input')
+    task_history = gr.State([])
     audio_output = gr.Audio('temp_file.wav', label="Generated Audio (wav)", type='filepath', autoplay=False)
     
-    def translate(audio):
-        print("__Wysyłam nagranie do whisper!")
-        transcription = whisper_model.transcribe(audio, language="pl")
-        return transcription["text"]
-    
-    def read_text(text):
-        print("Tutaj jest tekst to przeczytania!", text[-1][-1])
-        inputs = tokenizer(text[-1][-1], return_tensors="pt").to("cuda")
-        with torch.no_grad():
-            output = tts_model(**inputs).waveform.squeeze().cpu().numpy()
-        sf.write('temp_file.wav', output, tts_model.config.sampling_rate)
-        return 'temp_file.wav'
-    
-    def user(audio_data, history):
-        if audio_data:
-            user_message = translate(audio_data)
-            print("USER!:")
-            print("", history + [[user_message, None]])
-            return history + [[user_message, None]]
-
-    def parse_history(hist):
-        history_ = ""
-        for q, a in hist:
-            history_ += f"<user>: {q } \n"
-            if a:
-                history_ += f"<assistant>: {a} \n"
-        return history_
-
-    def bot(history):
-        print(f"When: {datetime.today().strftime('%Y-%m-%d %H:%M:%S')}")
-        prompt = f"Jesteś AI assystentem. Odpowiadaj krótko i po polsku. {parse_history(history)}. <assistant>:"
-        stream = llm(prompt, **params)
-        history[-1][1] = ""
-        answer_save = ""
-        for character in stream:
-            history[-1][1] += character
-            answer_save += character
-            time.sleep(0.005)
-            yield history
-
-    submit_audio.click(user, [audio_input, chatbot], [chatbot], queue=False).then(bot, chatbot, chatbot).then(read_text, chatbot, audio_output)
-    clear.click(lambda: None, None, chatbot, queue=False)
+    with gr.Row():
+        submit_btn = gr.Button("🚀 Wyślij tekst")
+
+    with gr.Row():
+        audio_upload = gr.Audio(source="microphone", type="filepath", show_label=False)
+        submit_audio_btn = gr.Button("🎙️ Wyślij audio")
+
+    submit_btn.click(predict, [query, chatbot, task_history], [chatbot], show_progress=True)
+    submit_audio_btn.click(predict, [audio_upload, chatbot, task_history], [chatbot], show_progress=True).then(update_audio, chatbot, audio_output)
+
+chat_demo.queue().launch(share=False)
+
 
 
 ##### Audio Gen ####