Update app.py

app.py

@@ -1,40 +1,55 @@
-import torch
 import gradio as gr
-from transformers import (
-    AutomaticSpeechRecognitionPipeline,
-    WhisperForConditionalGeneration,
-    WhisperTokenizer,
-    WhisperProcessor,
-)
-from peft import PeftModel, PeftConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from typing import List, Tuple
+import torch
 
-peft_model_id = "Moustapha91/whisper-small-wolof"
-language = "French"
-task = "transcribe"
 
-peft_config = PeftConfig.from_pretrained(peft_model_id)
-model = WhisperForConditionalGeneration.from_pretrained(
-    peft_config.base_model_name_or_path, 
-    device_map="auto"  # On supprime la quantization en 8 bits
-)
+model_name = "Hawoly18/Adia_Llama3.1"
+
+# Vérifier si un GPU est disponible
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def respond(
+    message: str,
+    history: List[Tuple[str, str]],
+    system_message: str,
+    max_tokens: int,
+    temperature: float,
+    top_p: float,
+) -> str:
+    
+    prompt = system_message
+    for user_msg, assistant_msg in history:
+        prompt += f"\nUser: {user_msg}\nAssistant: {assistant_msg}"
+    prompt += f"\nUser: {message}\nAssistant:"
+
+    
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=True,
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("Assistant:")[-1].strip()
+    return response
+
 
-model = PeftModel.from_pretrained(model, peft_model_id)
-tokenizer = WhisperTokenizer.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
-processor = WhisperProcessor.from_pretrained(peft_config.base_model_name_or_path, language=language, task=task)
-feature_extractor = processor.feature_extractor
-forced_decoder_ids = processor.get_decoder_prompt_ids(language=language, task=task)
-pipe = AutomaticSpeechRecognitionPipeline(model=model, tokenizer=tokenizer, feature_extractor=feature_extractor)
-
-def transcribe(audio):
-    text = pipe(audio, generate_kwargs={"forced_decoder_ids": forced_decoder_ids}, max_new_tokens=255)["text"]
-    return text
-
-iface = gr.Interface(
-    fn=transcribe,
-    inputs=gr.Audio(type="filepath"),  # On supprime 'source' pour éviter l'erreur
-    outputs="text",
-    title="PEFT LoRA + Whisper Small Wolof",
-    description="Realtime demo for Wolof speech recognition using `PEFT-LoRA` fine-tuned Whisper Small model.",
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),  # Fixed syntax error
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
+    title="Chatbot Interface"
 )
 
-iface.launch(share=True)
+if __name__ == "__main__":
+    demo.launch()