Update app.py

app.py

@@ -1,166 +1,22 @@
-import gradio as gr
-import spaces
-import torch
-from transformers import AutoTokenizer,VitsModel
-import os
-import numpy as np  
-# Use a pipeline as a high-level helper
 from transformers import pipeline
+import gradio as gr
 
+# تحميل النموذج
 pipe = pipeline("text-generation", model="wasmdashai/Seed-Coder-8B-Instruct-V1")
-messages = [
-    {"role": "user", "content": "Who are you?"},
-]
-pipe(messages)
-token=os.environ.get("key_")
-tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
-models= {}
-
-import noisereduce as nr
-
-import torch
-from typing import Any, Callable, Optional, Tuple, Union,Iterator
-
-import torch.nn as nn # Import the missing module
-def remove_noise_nr(audio_data,sr=16000):
-    """يزيل الضوضاء باستخدام مكتبة noisereduce."""
-    reduced_noise = nr.reduce_noise(y=audio_data,hop_length=256, sr=sr)
-    return reduced_noise
-
-def _inference_forward_stream(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        speaker_embeddings: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        padding_mask: Optional[torch.Tensor] = None,
-        chunk_size: int = 32,  # Chunk size for streaming output
-        is_streaming: bool = True,
-    ) -> Iterator[torch.Tensor]:
-        """Generates speech waveforms in a streaming fashion."""
-        if attention_mask is not None:
-            padding_mask = attention_mask.unsqueeze(-1).float()
-        else:
-            padding_mask = torch.ones_like(input_ids).unsqueeze(-1).float()
-
-
-
-        text_encoder_output = self.text_encoder(
-            input_ids=input_ids,
-            padding_mask=padding_mask,
-            attention_mask=attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = text_encoder_output[0] if not return_dict else text_encoder_output.last_hidden_state
-        hidden_states = hidden_states.transpose(1, 2)
-        input_padding_mask = padding_mask.transpose(1, 2)
-
-        prior_means = text_encoder_output[1] if not return_dict else text_encoder_output.prior_means
-        prior_log_variances = text_encoder_output[2] if not return_dict else text_encoder_output.prior_log_variances
-
-        if self.config.use_stochastic_duration_prediction:
-            log_duration = self.duration_predictor(
-                hidden_states,
-                input_padding_mask,
-                speaker_embeddings,
-                reverse=True,
-                noise_scale=self.noise_scale_duration,
-            )
-        else:
-            log_duration = self.duration_predictor(hidden_states, input_padding_mask, speaker_embeddings)
-
-        length_scale = 1.0 / self.speaking_rate
-        duration = torch.ceil(torch.exp(log_duration) * input_padding_mask * length_scale)
-        predicted_lengths = torch.clamp_min(torch.sum(duration, [1, 2]), 1).long()
-
-
-        # Create a padding mask for the output lengths of shape (batch, 1, max_output_length)
-        indices = torch.arange(predicted_lengths.max(), dtype=predicted_lengths.dtype, device=predicted_lengths.device)
-        output_padding_mask = indices.unsqueeze(0) < predicted_lengths.unsqueeze(1)
-        output_padding_mask = output_padding_mask.unsqueeze(1).to(input_padding_mask.dtype)
-
-        # Reconstruct an attention tensor of shape (batch, 1, out_length, in_length)
-        attn_mask = torch.unsqueeze(input_padding_mask, 2) * torch.unsqueeze(output_padding_mask, -1)
-        batch_size, _, output_length, input_length = attn_mask.shape
-        cum_duration = torch.cumsum(duration, -1).view(batch_size * input_length, 1)
-        indices = torch.arange(output_length, dtype=duration.dtype, device=duration.device)
-        valid_indices = indices.unsqueeze(0) < cum_duration
-        valid_indices = valid_indices.to(attn_mask.dtype).view(batch_size, input_length, output_length)
-        padded_indices = valid_indices - nn.functional.pad(valid_indices, [0, 0, 1, 0, 0, 0])[:, :-1]
-        attn = padded_indices.unsqueeze(1).transpose(2, 3) * attn_mask
-
-        # Expand prior distribution
-        prior_means = torch.matmul(attn.squeeze(1), prior_means).transpose(1, 2)
-        prior_log_variances = torch.matmul(attn.squeeze(1), prior_log_variances).transpose(1, 2)
-
-        prior_latents = prior_means + torch.randn_like(prior_means) * torch.exp(prior_log_variances) * self.noise_scale
-        latents = self.flow(prior_latents, output_padding_mask, speaker_embeddings, reverse=True)
-
-        spectrogram = latents * output_padding_mask
-        if is_streaming:
-            
-            for i in range(0, spectrogram.size(-1), chunk_size):
-                with torch.no_grad():
-                    wav=self.decoder(spectrogram[:,:,i : i + chunk_size] ,speaker_embeddings)
-                yield wav.squeeze().cpu().numpy()
-        else:
-
-              wav=self.decoder(spectrogram,speaker_embeddings)
-              yield  wav.squeeze().cpu().numpy()
-@spaces.GPU
-def  get_model(name_model):
-    global models
-    if name_model in   models:
-        return models[name_model]
-    models[name_model]=VitsModel.from_pretrained(name_model,token=token).cuda()
-    models[name_model].decoder.apply_weight_norm()
-    # torch.nn.utils.weight_norm(self.decoder.conv_pre)
-    # torch.nn.utils.weight_norm(self.decoder.conv_post)
-    for flow in models[name_model].flow.flows:
-        torch.nn.utils.weight_norm(flow.conv_pre)
-        torch.nn.utils.weight_norm(flow.conv_post)
-    return models[name_model]
-
-
-zero = torch.Tensor([0]).cuda()
-print(zero.device) # <-- 'cpu' 🤔
-import torch
-TXT="""السلام  عليكم  ورحمة الله وبركاتة  يا هلا وسهلا ومراحب بالغالي  اخباركم  طيبين ان شاء الله     ارحبوا  على العين والراس     """
-@spaces.GPU
-def   modelspeech(text=TXT,name_model="wasmdashai/vits-ar-sa-huba-v2",speaking_rate=16000):
-     
-    
-     inputs = tokenizer(text, return_tensors="pt")
-     model=get_model(name_model)
-     model.speaking_rate=speaking_rate
-     with torch.no_grad():
-        wav=list(_inference_forward_stream(model,input_ids=inputs.input_ids.cuda(),attention_mask=inputs.attention_mask.cuda(),speaker_embeddings= None,is_streaming=False))[0]
-     # with torch.no_grad():
-     #      wav = model(input_ids=inputs["input_ids"].cuda()).waveform.cpu().numpy().reshape(-1)#.detach()
-          
-     return  (model.config.sampling_rate,wav),(model.config.sampling_rate,remove_noise_nr(wav))
-
-model_choices = gr.Dropdown(
-                            choices=[
-                                
-                                "wasmdashai/vits-ar-sa-huba-v1",
-                                 "wasmdashai/vits-ar-sa-huba-v2",
-                                 
-                                 "wasmdashai/vits-ar-sa-A",
-                                "wasmdashai/vits-ar-ye-sa",
-                                "wasmdashai/vits-ar-sa-M-v1",
-                                "wasmdashai/vits-ar-sa-M-v2"
-                                
-                                  
-                            ],
-                            label="اختر النموذج",
-                            value="wasmdashai/vits-ar-sa-huba-v2",
-                        )
 
-demo = gr.Interface(fn=modelspeech, inputs=["text",model_choices,gr.Slider(0, 1, step=0.1,value=0.8)], outputs=["audio","audio"])
-demo.queue()
-demo.launch()
+# دالة توليد الردود
+def chat_with_model(user_input):
+    messages = [
+        {"role": "user", "content": user_input},
+    ]
+    output = pipe(messages, max_new_tokens=200, do_sample=True)
+    return output[0]['generated_text']
+
+# واجهة Gradio
+gr.Interface(
+    fn=chat_with_model,
+    inputs=gr.Textbox(lines=2, placeholder="اكتب سؤالك هنا..."),
+    outputs="text",
+    title="Seed-Coder Chat",
+    description="نموذج Seed-Coder للإجابة على الأسئلة باستخدام نموذج توليد النصوص"
+).launch()