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app (1).py

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+import gradio as gr
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from deep_translator import GoogleTranslator
+from gtts import gTTS
+import os
+
+# Initialize the model and tokenizer
+peft_model_id = "saludobuenas/test3"
+config = PeftConfig.from_pretrained(peft_model_id)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True)
+model.to('cpu')
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+# Your fixed prompt
+fixed_prompt = "###input: Game progress:  Jugador 1 played [6|6]  Jugador 2 played [4|6].Board State: (6,4).Player 1 tiles: [5|6], [4|5], [0|4], [3|6], [4|4],[2|6], [1|3].Given the current board state and Player 1’s tiles, which tile should he play and why? ###output:"
+
+def translate_to_spanish(text):
+    translation = GoogleTranslator(source='auto', target='es').translate(text=text)
+    return translation
+
+def texto_a_voz(texto):
+    tts = gTTS(text=texto, lang='es')
+    audio_file = "/tmp/temp.wav"
+    tts.save(audio_file)
+    return audio_file
+
+def process_prompt():
+    input_ids = tokenizer.encode(fixed_prompt, return_tensors='pt')
+    output = model.generate(input_ids=input_ids, max_length=210)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    output_text = generated_text.split("###output:")[1].strip()
+    translation = translate_to_spanish(output_text)
+    audio_file = texto_a_voz(translation)
+    return output_text, translation, audio_file
+
+# Since we're not using inputs, we define a function that launches the processing
+def launch_response():
+    return process_prompt()
+
+# Define the Gradio interface without inputs
+iface = gr.Interface(fn=launch_response,
+                     inputs=[],
+                     outputs=[gr.Textbox(label="Generated Response"),
+                              gr.Textbox(label="Translation"),
+                              gr.Audio(label="Text-to-Speech")],
+                     title="Model Response Generator",
+                     description="Displays the model's response, its Spanish translation, and the text-to-speech audio for the fixed prompt.",
+                     examples=[]).launch()

requirements (1).txt

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+streamlit
+gradio
+peft
+bitsandbytes
+sentencepiece
+httpx
+httpcore
+pymongo
+deep-translator
+IPython
+gTTS
+pygame