Create app.py

app.py

@@ -0,0 +1,157 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from llama_cpp import Llama
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import uvicorn
+import re
+from dotenv import load_dotenv
+import spaces
+
+load_dotenv()
+
+app = FastAPI()
+
+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_configs = [
+    {"repo_id": "Ffftdtd5dtft/gpt2-xl-Q2_K-GGUF", "filename": "gpt2-xl-q2_k.gguf", "name": "GPT-2 XL"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-8B Instruct"},
+    {"repo_id": "Ffftdtd5dtft/gemma-2-9b-it-Q2_K-GGUF", "filename": "gemma-2-9b-it-q2_k.gguf", "name": "Gemma 2-9B IT"},
+    {"repo_id": "Ffftdtd5dtft/gemma-2-27b-Q2_K-GGUF", "filename": "gemma-2-27b-q2_k.gguf", "name": "Gemma 2-27B"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-Q2_K-GGUF", "filename": "phi-3-mini-128k-instruct-q2_k.gguf", "name": "Phi-3 Mini 128K Instruct"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-8B-Q2_K-GGUF", "filename": "meta-llama-3.1-8b-q2_k.gguf", "name": "Meta Llama 3.1-8B"},
+    {"repo_id": "Ffftdtd5dtft/Qwen2-7B-Instruct-Q2_K-GGUF", "filename": "qwen2-7b-instruct-q2_k.gguf", "name": "Qwen2 7B Instruct"},
+    {"repo_id": "Ffftdtd5dtft/starcoder2-3b-Q2_K-GGUF", "filename": "starcoder2-3b-q2_k.gguf", "name": "Starcoder2 3B"},
+    {"repo_id": "Ffftdtd5dtft/Qwen2-1.5B-Instruct-Q2_K-GGUF", "filename": "qwen2-1.5b-instruct-q2_k.gguf", "name": "Qwen2 1.5B Instruct"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-q2_k.gguf", "name": "Meta Llama 3.1-70B"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"},
+    {"repo_id": "Ffftdtd5dtft/Hermes-3-Llama-3.1-8B-IQ1_S-GGUF", "filename": "hermes-3-llama-3.1-8b-iq1_s-imat.gguf", "name": "Hermes 3 Llama 3.1-8B"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3.5-mini-instruct-Q2_K-GGUF", "filename": "phi-3.5-mini-instruct-q2_k.gguf", "name": "Phi 3.5 Mini Instruct"},
+    {"repo_id": "Ffftdtd5dtft/Meta-Llama-3.1-70B-Instruct-Q2_K-GGUF", "filename": "meta-llama-3.1-70b-instruct-q2_k.gguf", "name": "Meta Llama 3.1-70B Instruct"},
+    {"repo_id": "Ffftdtd5dtft/codegemma-2b-IQ1_S-GGUF", "filename": "codegemma-2b-iq1_s-imat.gguf", "name": "Codegemma 2B"},
+    {"repo_id": "Ffftdtd5dtft/Phi-3-mini-128k-instruct-IQ2_XXS-GGUF", "filename": "phi-3-mini-128k-instruct-iq2_xxs-imat.gguf", "name": "Phi 3 Mini 128K Instruct XXS"},
+    {"repo_id": "Ffftdtd5dtft/TinyLlama-1.1B-Chat-v1.0-IQ1_S-GGUF", "filename": "tinyllama-1.1b-chat-v1.0-iq1_s-imat.gguf", "name": "TinyLlama 1.1B Chat"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-NeMo-Minitron-8B-Base-IQ1_S-GGUF", "filename": "mistral-nemo-minitron-8b-base-iq1_s-imat.gguf", "name": "Mistral NeMo Minitron 8B Base"},
+    {"repo_id": "Ffftdtd5dtft/Mistral-Nemo-Instruct-2407-Q2_K-GGUF", "filename": "mistral-nemo-instruct-2407-q2_k.gguf", "name": "Mistral Nemo Instruct 2407"}
+]
+
+class ModelManager:
+    def __init__(self):
+        self.loaded = False
+
+    def load_model(self, model_config):
+        try:
+            return {"model": Llama.from_pretrained(repo_id=model_config['repo_id'], filename=model_config['filename']), "name": model_config['name']}
+        except Exception:
+            pass
+
+    def load_all_models(self):
+        if self.loaded:
+            return global_data['models']
+        
+        try:
+            with ThreadPoolExecutor() as executor:
+                futures = [executor.submit(self.load_model, config) for config in model_configs]
+                models = []
+                for future in as_completed(futures):
+                    model = future.result()
+                    if model:
+                        models.append(model)
+            
+            global_data['models'] = models
+            self.loaded = True
+            return models
+        except Exception:
+            pass
+
+model_manager = ModelManager()
+model_manager.load_all_models()
+
+class ChatRequest(BaseModel):
+    message: str
+    top_k: int = 50
+    top_p: float = 0.95
+    temperature: float = 0.7
+
+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:
+            seen_lines.add(line)
+            unique_lines.append(line)
+    return '\n'.join(unique_lines)
+
+def remove_repetitive_responses(responses):
+    seen = set()
+    unique_responses = []
+    for response in responses:
+        normalized_response = remove_duplicates(response['response'])
+        if normalized_response not in seen:
+            seen.add(normalized_response)
+            unique_responses.append(response)
+    return unique_responses
+
+def generate_chat_response(request, model_data):
+    model = model_data['model']
+    try:
+        user_input = normalize_input(request.message)
+        response = model(user_input, top_k=request.top_k, top_p=request.top_p, temperature=request.temperature)
+        return {"model": model_data['name'], "response": response}
+    except Exception:
+        pass
+
+@spaces.GPU(duration=0)
+async def generate(request: ChatRequest):
+    try:
+        responses = []
+        with ThreadPoolExecutor() as executor:
+            futures = [executor.submit(generate_chat_response, request, model_data) for model_data in global_data['models']]
+            for future in as_completed(futures):
+                try:
+                    response = future.result()
+                    if response:
+                        responses.append(response)
+                except Exception:
+                    pass
+        
+        if not responses:
+            raise HTTPException(status_code=500, detail="Error: No responses generated.")
+        
+        responses = remove_repetitive_responses(responses)
+        best_response = responses[0] if responses else {}
+        return {
+            "best_response": best_response,
+            "all_responses": responses
+        }
+    except Exception:
+        pass
+
+@app.api_route("/{method_name:path}", methods=["GET", "POST", "PUT", "DELETE", "PATCH"])
+async def handle_request(method_name: str):
+    try:
+        return {"message": "Request handled successfully"}
+    except Exception:
+        raise HTTPException(status_code=500, detail="Error: Internal Server Error")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)