This commit includes the following changes:

(1)Created a new main.py file: Established basic FastAPI settings to enhance application structure and scalability. This setup includes initial configurations and middleware setup, laying the groundwork for future development.
(2)Renamed the original main.py: Changed to original_main.py to preserve the previous version and provide a reference for legacy code, facilitating a smooth transition and ensuring no loss of important historical context.
(3)Refactored project structure: Maintained the existing piggy directory and introduced a chat directory. This separation of routers and APIs improves modularity, making it easier to manage and extend each project independently.
These changes aim to improve code organization and prepare the project for scalable development with FastAPI.

chat/router.py

@@ -22,27 +22,33 @@
 class Question(BaseModel):
     query: str
 
+router = APIRouter()
+
 @router.post("/generate_answer")
 def generate_answer(value: Question):
     try:
+        # Load the llama model and tokenizer from the pretrained model
         llama_model, llama_tokenizer = FastLanguageModel.from_pretrained(
-            model_name = "Antonio27/llama3-8b-4-bit-for-sugar",
-            max_seq_length = max_seq_length,
-            dtype = dtype,
-            load_in_4bit = load_in_4bit,
+            model_name="Antonio27/llama3-8b-4-bit-for-sugar",
+            max_seq_length=max_seq_length,
+            dtype=dtype,
+            load_in_4bit=load_in_4bit,
         )
 
+        # Load the gemma model and tokenizer from the pretrained model
         gemma_model, gemma_tokenizer = FastLanguageModel.from_pretrained(
-            model_name = "unsloth/gemma-2-9b-it-bnb-4bit",
-            max_seq_length = max_seq_length,
-            dtype = dtype,
-            load_in_4bit = load_in_4bit,
+            model_name="unsloth/gemma-2-9b-it-bnb-4bit",
+            max_seq_length=max_seq_length,
+            dtype=dtype,
+            load_in_4bit=load_in_4bit,
         )
 
+        # Prepare llama model for inference
         FastLanguageModel.for_inference(llama_model)
         llama_tokenizer.pad_token = llama_tokenizer.eos_token
         llama_tokenizer.add_eos_token = True
 
+        # Tokenize the input question for the llama model
         inputs = llama_tokenizer(
             [
                 alpaca_prompt.format(
@@ -57,21 +63,26 @@ def generate_answer(value: Question):
                 )
             ], return_tensors="pt").to("cuda")
 
+        # Generate output using the llama model
         outputs = llama_model.generate(**inputs, max_new_tokens=256, temperature=0.6)
         decoded_outputs = llama_tokenizer.batch_decode(outputs)
 
+        # Extract the response text
         response_text = decoded_outputs[0]
 
+        # Use regex to find the response section in the output
         match = re.search(r"### Response:(.*?)(?=\n###|$)", response_text, re.DOTALL)
         if match:
             initial_response = match.group(1).strip()
         else:
             initial_response = ""
 
+        # Prepare gemma model for inference
         FastLanguageModel.for_inference(gemma_model)
         gemma_tokenizer.pad_token = gemma_tokenizer.eos_token
         gemma_tokenizer.add_eos_token = True
 
+        # Tokenize the initial response for the gemma model
         inputs = gemma_tokenizer(
             [
                 alpaca_prompt.format(
@@ -87,17 +98,21 @@ def generate_answer(value: Question):
                 )
             ], return_tensors="pt").to("cuda")
 
+        # Generate adjusted output using the gemma model
         outputs = gemma_model.generate(**inputs, max_new_tokens=256, temperature=0.6)
         decoded_outputs = gemma_tokenizer.batch_decode(outputs)
 
+        # Extract the adjusted response text
         response_text = decoded_outputs[0]
 
+        # Use regex to find the response section in the output
         match = re.search(r"### Response:(.*?)(?=\n###|$)", response_text, re.DOTALL)
         if match:
             adjusted_response = match.group(1).strip()
         else:
             adjusted_response = ""
 
+        # Return the final adjusted response in a success dictionary
         return {
             'success': True,
             'response': {

main.py

@@ -6,17 +6,21 @@
 from chat.router import router as chat_router
 # from piggy.router import router as piggy_router
 
+# Create a FastAPI application instance with custom documentation URL
 app = FastAPI(
     docs_url="/sugar-ai/docs",
 )
 
+# Include the chat router with a specified prefix for endpoint paths
 app.include_router(chat_router, prefix="/sugar-ai/chat")
+# Include the piggy router with a specified prefix for endpoint paths (currently commented out)
 # app.include_router(piggy_router, prefix="/sugar-ai/piggy")
 
+# Add CORS middleware to allow cross-origin requests from any origin
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
+    allow_origins=["*"],  # Allow requests from any origin
+    allow_credentials=True,  # Allow sending of credentials (e.g., cookies)
+    allow_methods=["*"],  # Allow all HTTP methods
+    allow_headers=["*"],  # Allow all headers
 )