Update app.py

app.py

@@ -2,26 +2,21 @@ import gradio as gr
 from gtts import gTTS
 from io import BytesIO
 import IPython.display as ipd
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-codellama_model = gr.Interface.load("models/meta-llama/CodeLlama-7b-Python-hf")
-deepseek_model = gr.Interface.load("models/deepseek-ai/deepseek-coder-1.3b-instruct")
+# Load your Hugging Face model and tokenizer
+model_name = "soufyane/gemma_data_science"
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-def process_text_codellama(input_text):
-    return codellama_model.predict(input_text)
+def process_text_gemma(input_text):
+    input_ids = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)["input_ids"]
+    output_ids = model.generate(input_ids)
+    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return response
 
-def process_speech_codellama(audio):
-    response = codellama_model.predict(audio)
-    tts = gTTS(text=response, lang='en')
-    fp = BytesIO()
-    tts.write_to_fp(fp)
-    fp.seek(0)
-    return ipd.Audio(fp.read(), autoplay=True)
-
-def process_text_deepseek(input_text):
-    return deepseek_model.predict(input_text)
-
-def process_speech_deepseek(audio):
-    response = deepseek_model.predict(audio)
+def process_speech_gemma(audio):
+    response = process_text_gemma(audio)
     tts = gTTS(text=response, lang='en')
     fp = BytesIO()
     tts.write_to_fp(fp)
@@ -29,14 +24,13 @@ def process_speech_deepseek(audio):
     return ipd.Audio(fp.read(), autoplay=True)
 
 def main(input_text):
-    if input_text[1]:
-        return process_text_deepseek(input_text[0]), process_speech_deepseek(input_text[0])
-    else:
-        return process_text_codellama(input_text[0]), process_speech_codellama(input_text[0])
+    return process_text_gemma(input_text[0]), process_speech_gemma(input_text[0])
 
 gr.Interface(
     fn=main,
-    inputs=["text", "checkbox"],  # ["input text", "enable voice input"]
+    inputs=["text"],
     outputs=["text", "audio"],
+    title="Gemma Data Science Model",
+    description="This is a text-to-text model for data science tasks.",
     live=True
-).launch()
+).launch()