Create app.py

app.py

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+from pydantic import BaseModel
+from llama_cpp import Llama
+from concurrent.futures import ThreadPoolExecutor
+import re
+import os
+import gradio as gr
+from dotenv import load_dotenv
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
+import spaces
+import urllib3
+
+urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
+
+app = FastAPI()
+load_dotenv()
+
+HUGGINGFACE_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+global_data = {
+    'model': None,
+    '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_configs = [
+    {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3.5-mini-instruct-Q2_K-GGUF", "filename": "phi-3.5-mini-instruct-q2_k.gguf"},
+    {"repo_id": "Ffftdtd5dtft/TinyLlama-1.1B-Chat-v1.0-IQ1_S-GGUF", "filename": "tinyllama-1.1b-chat-v1.0-iq1_s-imat.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Minitron-8B-Base-IQ1_S-GGUF", "filename": "mistral-nemo-minitron-8b-base-iq1_s-imat.gguf"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf"}
+]
+
+class ModelManager:
+    def __init__(self):
+        self.model = None
+
+    def load_models(self):
+        models = []
+        for config in model_configs:
+            try:
+                model = Llama.from_pretrained(repo_id=config['repo_id'], filename=config['filename'], use_auth_token=HUGGINGFACE_TOKEN)
+                models.append(model)
+            except Exception as e:
+                pass
+        self.model = models
+
+model_manager = ModelManager()
+model_manager.load_models()
+global_data['model'] = model_manager.model
+
+class ChatRequest(BaseModel):
+    message: str
+
+def normalize_input(input_text):
+    return input_text.strip()
+
+def remove_duplicates(text):
+    text = re.sub(r'(Hello there, how are you\? \[/INST\]){2,}', 'Hello there, how are you? [/INST]', text)
+    text = re.sub(r'(How are you\? \[/INST\]){2,}', 'How are you? [/INST]', text)
+    text = text.replace('[/INST]', '')
+    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)
+
+@spaces.GPU()
+async def generate_combined_response(inputs):
+    combined_response = ""
+    for model in global_data['model']:
+        try:
+            response = model(inputs)
+            combined_response += remove_duplicates(response['choices'][0]['text']) + "\n"
+        except Exception as e:
+            pass
+    return combined_response
+
+async def process_message(message):
+    inputs = normalize_input(message)
+    combined_response = await generate_combined_response(inputs)
+    formatted_response = ""
+    for line in combined_response.split("\n"):
+        formatted_response += f"{line}\n\n"
+    return formatted_response
+
+@app.post("/generate_multimodel")
+async def api_generate_multimodel(request: Request):
+    data = await request.json()
+    message = data["message"]
+    formatted_response = await process_message(message)
+    return JSONResponse({"response": formatted_response})
+
+iface = gr.Interface(
+    fn=process_message,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your message here..."),
+    outputs=gr.Markdown(),
+    title="Multi-Model LLM API",
+    description="Enter a message and get responses from a unified model.",
+)
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    iface.launch(server_port=port)