app.py

import torch
from transformers import (
 AutoTokenizer,
 AutoModelForCausalLM,
 AutoModelForSeq2SeqLM,
 AutoProcessor,
 AutoModelForSpeechSeq2Seq,
 AutoModelForTextToWaveform
)
from diffusers import DiffusionPipeline
import time
import os
from dotenv import load_dotenv
from huggingface_hub import HfApi, HfFolder, Repository
import gradio as gr

load_dotenv()

def prune_model(model, amount=0.5):
 from torch.nn.utils import prune
 for name, module in model.named_modules():
 if isinstance(module, (torch.nn.Linear, torch.nn.Conv2d)):
 prune.l1_unstructured(module, name='weight', amount=amount)
 prune.remove(module, 'weight')
 return model

def quantize_to_q1_with_min(tensor, min_value=-1):
 tensor = torch.sign(tensor)
 tensor[tensor < min_value] = min_value
 return tensor

def quantize_model_to_q1_with_min(model, min_value=-1):
 for name, param in model.named_parameters():
 if param.dtype in [torch.float32, torch.float16]:
 with torch.no_grad():
 param.copy_(quantize_to_q1_with_min(param.data, min_value))

def disable_unnecessary_components(model):
 for name, module in model.named_modules():
 if isinstance(module, torch.nn.Dropout):
 module.p = 0.0
 elif isinstance(module, torch.nn.BatchNorm1d):
 module.eval()

def ultra_max_compress(model):
 model = prune_model(model, amount=0.8)
 quantize_model_to_q1_with_min(model, min_value=-0.05)
 disable_unnecessary_components(model)
 with torch.no_grad():
 for name, param in model.named_parameters():
 if param.requires_grad:
 param.requires_grad = False
 param.data = torch.nn.functional.hardtanh(param.data, min_val=-1.0, max_val=1.0)
 param.data = param.data.half()
 try:
 model = torch.jit.script(model)
 except Exception:
 pass
 prune_model(model, amount=0.9)
 model.eval()
 for buffer_name, buffer in model.named_buffers():
 if buffer.numel() == 0:
 model._buffers.pop(buffer_name)
 return model

def optimize_model_resources(model):
 torch.set_grad_enabled(False)
 model.eval()
 for name, param in model.named_parameters():
 param.requires_grad = False
 if param.dtype == torch.float32:
 param.data = param.data.half()
 if hasattr(model, 'config'):
 if hasattr(model.config, 'max_position_embeddings'):
 model.config.max_position_embeddings = min(model.config.max_position_embeddings, 512)
 if hasattr(model.config, 'hidden_size'):
 model.config.hidden_size = min(model.config.hidden_size, 768)
 model = torch.jit.optimize_for_inference(model)
 return model

def generate_random_responses(model, tokenizer, prompt, num_responses=5, max_length=50):
 responses = []
 for _ in range(num_responses):
 input_ids = tokenizer.encode(prompt, return_tensors="pt")
 output = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=50)
 response = tokenizer.decode(output[0], skip_special_tokens=True)
 responses.append(response)
 return responses

def patched_distilbert_forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None):
 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 outputs = DistilBertModel.forward(self, input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
 if not return_dict:
 output_tuple = []
 for v in [outputs.last_hidden_state, outputs.hidden_states, outputs.attentions]:
 if v is not None:
 output_tuple.append(v)
 return tuple(output_tuple)
 return outputs

def patched_forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None):
 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 outputs = self.distilbert(input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
 hidden_state = outputs[0]
 pooled_output = self.pre_classifier(hidden_state[:, 0])
 pooled_output = self.dropout(pooled_output)
 logits = self.classifier(pooled_output)
 if not return_dict:
 output = (logits,) + outputs[1:]
 return output
 return logits

def patched_roberta_forward(self, input_ids=None, attention_mask=None, head_mask=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None):
 return_dict = return_dict if return_dict is not None else self.config.use_return_dict
 outputs = self.roberta(input_ids, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
 hidden_state = outputs[0]
 pooled_output = hidden_state[:, 0]
 pooled_output = self.dropout(pooled_output)
 logits = self.classifier(pooled_output)
 if not return_dict:
 output = (logits,) + outputs[1:]
 return output
 return logits

def optimize_for_low_resources(model):
 model = ultra_max_compress(model)
 model = optimize_model_resources(model)
 model.config.max_position_embeddings = 256
 model.config.hidden_size = 384
 return model

def optimize_for_very_low_resources(model):
 model = ultra_max_compress(model)
 model = optimize_model_resources(model)
 model.config.max_position_embeddings = 128
 model.config.hidden_size = 256
 return model

def remove_unused_model_components(model):
 for name, param in model.named_parameters():
 if param.numel() == 0:
 model._parameters.pop(name)
 return model

def auto_train_model(model, train_data, epochs=3):
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
 model.train()
 for epoch in range(epochs):
 for batch in train_data:
 inputs, labels = batch
 optimizer.zero_grad()
 outputs = model(**inputs, labels=labels)
 loss = outputs.loss
 loss.backward()
 optimizer.step()
 return model

def apply_extreme_filters(model):
 model = ultra_max_compress(model)
 model = optimize_model_resources(model)
 model.config.max_position_embeddings = 128
 model.config.hidden_size = 256
 model = torch.jit.optimize_for_inference(model)
 model = prune_model(model, amount=0.95)
 quantize_model_to_q1_with_min(model, min_value=-0.1)
 return model

def reduce_latency(model, tokenizer, prompt, num_responses=5, max_length=50):
 responses = []
 start_time = time.time()
 for _ in range(num_responses):
 input_ids = tokenizer.encode(prompt, return_tensors="pt")
 output = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=50)
 response = tokenizer.decode(output[0], skip_special_tokens=True)
 responses.append(response)
 end_time = time.time()
 latency = (end_time - start_time) / num_responses * 1000
 return responses, latency

def create_gpt_distill_model():
 gpt_model = GPT2LMHeadModel.from_pretrained("gpt2")
 gpt_tokenizer = AutoTokenizer.from_pretrained("gpt2")
 return gpt_model, gpt_tokenizer

def create_gemma_distill_model():
 gemma_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b")
 gemma_tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b")
 return gemma_model, gemma_tokenizer

def measure_performance(model, tokenizer, sequence_length=20, num_tokens=100):
 inputs = tokenizer("A" * sequence_length, return_tensors="pt")
 start_time = time.time()
 for _ in range(num_tokens):
 model.generate(**inputs)
 end_time = time.time()
 latency = (end_time - start_time) / num_tokens * 1000
 tokens_per_second = num_tokens / (end_time - start_time)
 return latency, tokens_per_second

def apply_diffusion_pipeline(prompt):
 diffusion_pipeline = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell")
 images = diffusion_pipeline(prompt).images
 return images

def generate_responses_with_diffusion(prompt, use_diffusion):
 if "imagina" in prompt.lower() or "imagine" in prompt.lower():
 images = apply_diffusion_pipeline(prompt)
 return images
 return None

def generate_summary_with_bart(prompt):
 tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large-cnn")
 model = AutoModelForSeq2SeqLM.from_pretrained("facebook/bart-large-cnn")
 inputs = tokenizer.encode(prompt, return_tensors="pt")
 summary_ids = model.generate(inputs, max_length=130, min_length=30, length_penalty=2.0, num_beams=4, early_stopping=True)
 summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
 return summary

def generate_responses_with_bart(prompt):
 if "resumir" in prompt.lower() or "resumime" in prompt.lower():
 summary = generate_summary_with_bart(prompt)
 return summary
 return None

def apply_whisper_pipeline(prompt):
 processor = AutoProcessor.from_pretrained("openai/whisper-small")
 model = AutoModelForSpeechSeq2Seq.from_pretrained("openai/whisper-small")
 inputs = processor(prompt, return_tensors="pt")
 outputs = model.generate(**inputs)
 transcription = processor.batch_decode(outputs, skip_special_tokens=True)
 return transcription

def generate_transcription_with_whisper(prompt):
 if "transcribe" in prompt.lower() or "transcribime" in prompt.lower():
 transcription = apply_whisper_pipeline(prompt)
 return transcription
 return None

def apply_translation_pipeline(prompt):
 tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
 model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-base")
 inputs = tokenizer.encode(prompt, return_tensors="pt")
 translated_ids = model.generate(inputs, max_length=50)
 translated_text = tokenizer.decode(translated_ids[0], skip_special_tokens=True)
 return translated_text

def generate_translation_with_t5(prompt):
 if "traducir" in prompt.lower() or "traducime" in prompt.lower():
 translation = apply_translation_pipeline(prompt)
 return translation
 return None

def apply_musicgen_pipeline(prompt):
 tokenizer = AutoTokenizer.from_pretrained("facebook/musicgen-small")
 model = AutoModelForTextToWaveform.from_pretrained("facebook/musicgen-small")
 inputs = tokenizer(prompt, return_tensors="pt")
 audio = model.generate(inputs)
 return audio

def generate_music_with_musicgen(prompt):
 if "música" in prompt.lower() or "canción" in prompt.lower():
 music = apply_musicgen_pipeline(prompt)
 return music
 return None

def apply_musicgen_melody_pipeline(prompt):
 tokenizer = AutoTokenizer.from_pretrained("facebook/musicgen-melody")
 model = AutoModelForTextToWaveform.from_pretrained("facebook/musicgen-melody")
 inputs = tokenizer(prompt, return_tensors="pt")
 audio = model.generate(inputs)
 return audio

def generate_music_with_musicgen_melody(prompt):
 if "melodía" in prompt.lower() or "melodia" in prompt.lower():
 music = apply_musicgen_melody_pipeline(prompt)
 return music
 return None

def apply_stable_diffusion_pipeline(prompt):
 pipeline = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1")
 images = pipeline(prompt).images
 return images

def generate_responses_with_stable_diffusion(prompt):
 if "imagen" in prompt.lower() or "image" in prompt.lower():
 images = apply_stable_diffusion_pipeline(prompt)
 return images
 return None

def unify_models(*models):
 combined_model = torch.nn.ModuleList(models)
 return combined_model

def combined_filter(model):
 model = ultra_max_compress(model)
 model = optimize_model_resources(model)
 model.config.max_position_embeddings = 128
 model.config.hidden_size = 256
 model = torch.jit.optimize_for_inference(model)
 model = prune_model(model, amount=0.95)
 quantize_model_to_q1_with_min(model, min_value=-0.1)
 return model

def apply_filters_and_unify(model):
 model = combined_filter(model)
 model = remove_unused_model_components(model)
 return model

def upload_to_huggingface(model, repo_name):
 api = HfApi()
 try:
 api.create_repo(repo_id=repo_name, repo_type="model")
 except Exception:
 pass
 model.save_pretrained(repo_name)
 tokenizer.save_pretrained(repo_name)
 repo = Repository(repo_name)
 repo.push_to_hub()

def apply_extreme_filters_and_upload(model, repo_name):
 model = apply_extreme_filters(model)
 upload_to_huggingface(model, repo_name)

def start_gradio_interface():
 def process_prompt(prompt):
 response = {
 "summary": generate_responses_with_bart(prompt),
 "transcription": generate_transcription_with_whisper(prompt),
 "translation": generate_translation_with_t5(prompt),
 "music": generate_music_with_musicgen(prompt),
 "melody_music": generate_music_with_musicgen_melody(prompt),
 "image": generate_responses_with_stable_diffusion(prompt),
 "diffusion": generate_responses_with_diffusion(prompt, True)
 }
 return response

 interface = gr.Interface(
 fn=process_prompt,
 inputs=gr.Textbox(label="Enter Prompt"),
 outputs=[gr.Textbox(label="Summary"), gr.Textbox(label="Transcription"), gr.Textbox(label="Translation"),
 gr.Audio(label="Music"), gr.Audio(label="Melody Music"), gr.Image(label="Image"), gr.Image(label="Diffusion")],
 title="Multi-Function AI Model",
 description="Generate summaries, transcriptions, translations, music, melodies, images, and diffusion responses."
 )
 interface.launch()

start_gradio_interface()

model_infos = [
 {"model_name": "gpt2", "class": GPT2LMHeadModel},
 {"model_name": "google/gemma-2-9b", "class": AutoModelForCausalLM}
]

for model_info in model_infos:
 model = model_info["class"].from_pretrained(model_info["model_name"])
 tokenizer = AutoTokenizer.from_pretrained(model_info["model_name"])
 optimized_model, responses, latency = optimize_model_with_all_optimizations(model, tokenizer, "Sample prompt for optimization.")
 print(f"Model: {model_info['model_name']}")
 print(f"Latency: {latency:.2f} ms")
 print(f"Sample Responses: {responses}")

gpt_model, gpt_tokenizer = create_gpt_distill_model()
gemma_model, gemma_tokenizer = create_gemma_distill_model()

optimized_gpt_model, gpt_responses, gpt_latency = optimize_model_with_all_optimizations(gpt_model, gpt_tokenizer, "Sample prompt for GPT optimization.")
optimized_gemma_model, gemma_responses, gemma_latency = optimize_model_with_all_optimizations(gemma_model, gemma_tokenizer, "Sample prompt for Gemma optimization.")

combined_model = unify_models(optimized_gpt_model, optimized_gemma_model)

optimized_gpt_model_1gb = optimize_for_1gb_ram(optimized_gpt_model)
optimized_gemma_model_1gb = optimize_for_1gb_ram(optimized_gemma_model)
optimized_gpt_model_low = optimize_for_very_low_resources(optimized_gpt_model)
optimized_gemma_model_low = optimize_for_very_low_resources(optimized_gemma_model)
optimized_gpt_model_cpu = optimize_for_old_cpu(optimized_gpt_model)
optimized_gemma_model_cpu = optimize_for_old_cpu(optimized_gemma_model)
optimized_gpt_model_gpu = optimize_for_old_gpu(optimized_gpt_model)
optimized_gemma_model_gpu = optimize_for_old_gpu(optimized_gemma_model)

print("Models optimized for various resource constraints.")

diffusion_response = generate_responses_with_diffusion("Imagine a serene landscape", True)
if diffusion_response:
 print("Diffusion response generated.")

summary_response = generate_responses_with_bart("Resumir este texto para obtener un resumen efectivo.", True)
if summary_response:
 print("Summary response generated.")

transcription_response = generate_transcription_with_whisper("Transcribe this audio file.", True)
if transcription_response:
 print("Transcription response generated.")

translation_response = generate_translation_with_t5("Traducir este texto al inglés.", True)
if translation_response:
 print("Translation response generated.")

music_response = generate_music_with_musicgen("Música para una tarde tranquila.", True)
if music_response:
 print("Music response generated.")

melody_music_response = generate_music_with_musicgen_melody("Melodía para relajación.", True)
if melody_music_response:
 print("Melody music response generated.")

image_response = generate_responses_with_stable_diffusion("Imagen de un paisaje sereno.", True)
if image_response:
 print("Image response generated.")

upload_to_hf.rst.immbined_model, "Ffftdtd5dtft/my_model")