FastAPI Framework Setup Modification

chat/router.py

@@ -0,0 +1,125 @@
+from fastapi import APIRouter
+from pydantic import BaseModel
+
+from unsloth import FastLanguageModel
+import torch
+
+max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+
+alpaca_prompt = """Below is an instruction that describes a task, along with an input that provides additional context. Write a response that appropriately completes the request.
+
+### Instruction:
+{}
+
+### Input:
+{}
+
+### Response:
+{}"""
+
+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,
+        )
+
+        # 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,
+        )
+
+        # 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(
+                    f'''
+                    Your task is to answer children's questions using simple language.
+                    Explain any difficult words in a way a 3-year-old can understand.
+                    Keep responses under 60 words.
+                    \n\nQuestion: {value.query}
+                    ''',  # instruction
+                    "",  # input
+                    "",  # output - leave this blank for generation!
+                )
+            ], 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###|$)", response_text, re.DOTALL)
+        if match:
+            initial_response = match.group(1).strip()
+        else:
+            initial_response = ""
+
+        # Prepare gemma model for inference
+        FastLanguageModel.for_inference(gemma_model)
+        gemma_tokenizer.pad_token = gemma_tokenizer.eos_token
+        gemma_tokenizer.add_eos_token = True
+
+        # Tokenize the initial response for the gemma model
+        inputs = gemma_tokenizer(
+            [
+                alpaca_prompt.format(
+                    f'''
+                    Modify the given content for a 5-year-old.
+                    Use simple words and phrases.
+                    Remove any repetitive information.
+                    Keep responses under 50 words.
+                    \n\nGiven Content: {initial_response}
+                    ''',  # instruction
+                    "",  # input
+                    "",  # output - leave this blank for generation!
+                )
+            ], return_tensors="pt").to("cuda")
+
+        # Generate adjusted output using the gemma model
+        outputs = gemma_model.generate(**inputs, max_new_tokens=256, temperature=0.6)
+        decoded_outputs = gemma_tokenizer.batch_decode(outputs)
+
+        # Extract the adjusted response text
+        response_text = decoded_outputs[0]
+
+        # Use regex to find the response section in the output
+        match = re.search(r"### Response:(.*?)(?=\n###|$)", 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': {
+                "result": adjusted_response
+            }
+        }
+
+    except Exception as e:
+        return {'success': False, 'response': str(e)}
+

main.py

@@ -1,27 +1,26 @@
-
-from transformers import GPT2Tokenizer, GPT2LMHeadModel
-
-
-# We should rename this
-class AI_Test:
-    def __init__(self):
-        pass
-
-    def generate_bot_response(self, question):
-        tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
-        model = GPT2LMHeadModel.from_pretrained("distilgpt2")
-
-        prompt = '''
-        Your task is to answer children's questions using simple language.
-        Explain any difficult words in a way a 3-year-old can understand.
-        Keep responses under 60 words.
-        \n\nQuestion:
-        '''
-
-        input_text = prompt + question
-
-        inputs = tokenizer.encode(input_text, return_tensors='pt')
-        outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
-        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-        return answer
+import os
+import uvicorn
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+
+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 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
+)

original_main.py

@@ -0,0 +1,27 @@
+
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+
+# We should rename this
+class AI_Test:
+    def __init__(self):
+        pass
+
+    def generate_bot_response(self, question):
+        tokenizer = GPT2Tokenizer.from_pretrained("distilgpt2")
+        model = GPT2LMHeadModel.from_pretrained("distilgpt2")
+
+        prompt = '''
+        Your task is to answer children's questions using simple language.
+        Explain any difficult words in a way a 3-year-old can understand.
+        Keep responses under 60 words.
+        \n\nQuestion:
+        '''
+
+        input_text = prompt + question
+
+        inputs = tokenizer.encode(input_text, return_tensors='pt')
+        outputs = model.generate(inputs, max_length=150, num_return_sequences=1)
+        answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return answer