Create app.py

app.py

@@ -0,0 +1,73 @@
+from transformers import (
+    AutoTokenizer,
+    AutoModelForSeq2SeqLM,
+    pipeline
+)
+from textblob import TextBlob as tb
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-base-seq2seq")
+pipe = pipeline(model="aware-ai/whisper-base-german")
+
+
+
+def translate(text):
+    blob = tb(text)
+    translation = str(blob.translate(from_lang='de',to='en'))
+    return translation
+
+def translate_to_de(text):
+    blob = tb(text)
+    translation = str(blob.translate(from_lang='en',to='de'))
+    return translation
+    
+def transcribe(audio):
+    text = pipe(audio)["text"]
+    return text
+
+def generate(input, knowledge):
+
+    if knowledge == '':
+        pass
+    else:
+        knowledge = translate(knowledge)
+        
+    input = translate(input)
+
+    top_p = 1
+    min_length = 8
+    max_length = 64
+
+    instruction = 'given a dialog context and related knowledge, you need to answer the question based on the knowledge.'
+
+    if knowledge != '':
+        knowledge = '[KNOWLEDGE] ' + knowledge
+
+    dialog = ' EOS '.join([input])
+    query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+
+    input_ids = tokenizer(f"{query}", return_tensors="pt").input_ids
+    outputs = model.generate(input_ids, min_length=int(
+        min_length), max_length=int(max_length), top_p=top_p, do_sample=True)
+    output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    output = translate_to_de(output)
+    return output
+
+with gr.Blocks() as app:
+
+    conocimiento = gr.Textbox(label='Conocimiento',lines=7,max_lines=7)
+
+    with gr.Row():
+        voice = gr.Audio(source='microphone',type='filepath')
+        send_button = gr.Button(value='Transcribir')
+        button2 = gr.Button(value='Respuesta de la IA')
+
+    transc = gr.Textbox(label='Transcripción',value='',)
+    respuesta = gr.Textbox(label='Respuesta',interactive=False,value='')
+
+    send_button.click(fn=transcribe,inputs=voice,outputs=transc)
+    button2.click(fn=generate,inputs=[transc,conocimiento],outputs=respuesta)
+
+app.launch()