Create app.py

app.py

@@ -0,0 +1,77 @@
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from llama_cpp import Llama
+from concurrent.futures import ProcessPoolExecutor
+import uvicorn
+from dotenv import load_dotenv
+from difflib import SequenceMatcher
+
+load_dotenv()
+
+app = FastAPI()
+
+models = [
+    {"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"},
+]
+
+llms = []
+for model in models:
+    llm = Llama.from_pretrained(repo_id=model['repo_id'], filename=model['filename'])
+    llms.append(llm)
+
+class ChatRequest(BaseModel):
+    message: str
+    top_k: int = 50
+    top_p: float = 0.95
+    temperature: float = 0.7
+
+def generate_chat_response(request, llm):
+    try:
+        user_input = request.message
+        response = llm.create_chat_completion(
+            messages=[{"role": "user", "content": user_input}],
+            top_k=request.top_k,
+            top_p=request.top_p,
+            temperature=request.temperature
+        )
+        reply = response['choices'][0]['message']['content']
+        return reply
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def select_best_response(responses, request):
+    coherent_responses = filter_by_coherence(responses, request)
+    best_response = filter_by_similarity(coherent_responses)
+    return best_response
+
+def filter_by_coherence(responses, request):
+    return responses
+
+def filter_by_similarity(responses):
+    responses.sort(key=len, reverse=True)
+    best_response = responses[0]
+    for i in range(1, len(responses)):
+        ratio = SequenceMatcher(None, best_response, responses[i]).ratio()
+        if ratio < 0.9:
+            best_response = responses[i]
+            break
+    return best_response
+
+@app.post("/generate_chat")
+async def generate_chat(request: ChatRequest):
+    with ProcessPoolExecutor() as executor:
+        futures = [executor.submit(generate_chat_response, request, llm) for llm in llms]
+        responses = [future.result() for future in futures]
+    
+    if any("Error" in response for response in responses):
+        error_response = next(response for response in responses if "Error" in response)
+        raise HTTPException(status_code=500, detail=error_response)
+    
+    best_response = select_best_response(responses, request)
+    return {"response": best_response}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)