Create app.py

app.py

@@ -0,0 +1,183 @@
+from pydantic import BaseModel
+from llama_cpp_agent import Llama
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import re
+import httpx
+import asyncio
+import gradio as gr
+import os
+import gptcache
+from dotenv import load_dotenv
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+import uvicorn
+from threading import Thread
+
+load_dotenv()
+
+HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+global_data = {
+    'models': {},
+    'tokens': {
+        'eos': 'eos_token',
+        'pad': 'pad_token',
+        'padding': 'padding_token',
+        'unk': 'unk_token',
+        'bos': 'bos_token',
+        'sep': 'sep_token',
+        'cls': 'cls_token',
+        'mask': 'mask_token'
+    },
+    'model_metadata': {},
+    'max_tokens': 256,
+    'tokenizers': {},
+    'model_params': {},
+    'model_size': {},
+    'model_ftype': {},
+    'n_ctx_train': {},
+    'n_embd': {},
+    'n_layer': {},
+    'n_head': {},
+    'n_head_kv': {},
+    'n_rot': {},
+    'n_swa': {},
+    'n_embd_head_k': {},
+    'n_embd_head_v': {},
+    'n_gqa': {},
+    'n_embd_k_gqa': {},
+    'n_embd_v_gqa': {},
+    'f_norm_eps': {},
+    'f_norm_rms_eps': {},
+    'f_clamp_kqv': {},
+    'f_max_alibi_bias': {},
+    'f_logit_scale': {},
+    'n_ff': {},
+    'n_expert': {},
+    'n_expert_used': {},
+    'causal_attn': {},
+    'pooling_type': {},
+    'rope_type': {},
+    'rope_scaling': {},
+    'freq_base_train': {},
+    'freq_scale_train': {},
+    'n_ctx_orig_yarn': {},
+    'rope_finetuned': {},
+    'ssm_d_conv': {},
+    'ssm_d_inner': {},
+    'ssm_d_state': {},
+    'ssm_dt_rank': {},
+    'ssm_dt_b_c_rms': {},
+    'vocab_type': {},
+    'model_type': {}
+}
+
+model_configs = [
+    {"repo_id": "Hjgugugjhuhjggg/testing_semifinal-Q2_K-GGUF", "filename": "testing_semifinal-q2_k.gguf", "name": "testing"}
+]
+
+class ModelManager:
+    def __init__(self):
+        self.models = {}
+
+    def load_model(self, model_config):
+        if model_config['name'] not in self.models:
+            try:
+                self.models[model_config['name']] = Llama.from_pretrained(
+                    repo_id=model_config['repo_id'],
+                    filename=model_config['filename'],
+                    use_auth_token=HUGGINGFACE_TOKEN,
+                    n_threads=8,
+                    use_gpu=False
+                )
+            except Exception as e:
+                pass
+
+    def load_all_models(self):
+        with ThreadPoolExecutor() as executor:
+            for config in model_configs:
+                executor.submit(self.load_model, config)
+        return self.models
+
+model_manager = ModelManager()
+global_data['models'] = model_manager.load_all_models()
+
+class ChatRequest(BaseModel):
+    message: str
+
+def normalize_input(input_text):
+    return input_text.strip()
+
+def remove_duplicates(text):
+    lines = text.split('\n')
+    unique_lines = []
+    seen_lines = set()
+    for line in lines:
+        if line not in seen_lines:
+            unique_lines.append(line)
+            seen_lines.add(line)
+    return '\n'.join(unique_lines)
+
+def cache_response(func):
+    def wrapper(*args, **kwargs):
+        cache_key = f"{args}-{kwargs}"
+        if gptcache.get(cache_key):
+            return gptcache.get(cache_key)
+        response = func(*args, **kwargs)
+        gptcache.set(cache_key, response)
+        return response
+    return wrapper
+
+@cache_response
+def generate_model_response(model, inputs):
+    try:
+        response = model(inputs)
+        return remove_duplicates(response['choices'][0]['text'])
+    except Exception as e:
+        return ""
+
+def remove_repetitive_responses(responses):
+    unique_responses = {}
+    for response in responses:
+        if response['model'] not in unique_responses:
+            unique_responses[response['model']] = response['response']
+    return unique_responses
+
+async def process_message(message):
+    inputs = normalize_input(message)
+    with ThreadPoolExecutor() as executor:
+        futures = [
+            executor.submit(generate_model_response, model, inputs)
+            for model in global_data['models'].values()
+        ]
+        responses = [{'model': model_name, 'response': future.result()} for model_name, future in zip(global_data['models'].keys(), as_completed(futures))]
+    unique_responses = remove_repetitive_responses(responses)
+    formatted_response = ""
+    for model, response in unique_responses.items():
+        formatted_response += f"**{model}:**\n{response}\n\n"
+    return formatted_response
+
+app = FastAPI()
+
+@app.post("/generate")
+async def generate(request: ChatRequest):
+    response = await process_message(request.message)
+    return JSONResponse(content={"response": response})
+
+def run_uvicorn():
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+iface = gr.Interface(
+    fn=process_message,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
+    outputs=gr.Markdown(),
+    title="Multi-Model LLM API (CPU Optimized)",
+    description="Enter a message and get responses from multiple LLMs using CPU."
+)
+
+def run_gradio():
+    iface.launch(server_port=7860, prevent_thread_lock=True)
+
+if __name__ == "__main__":
+    Thread(target=run_uvicorn).start()
+    Thread(target=run_gradio).start()