Upload 6 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/final-gemma2b_SA-Q5_K.gguf filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -16,4 +16,4 @@ EXPOSE 7860
 
 ENV PYTHONUNBUFFERED=1
 
-CMD ["uvicorn", "app.main:app", "--port", "7860", "--host", "0.0.0.0"]
+CMD ["uvicorn", "app.main:app", "--port", "7860", "--host", "0.0.0.0"]

app/main.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+# coding: utf-8
+from os import listdir
+from os.path import isdir
+from fastapi import FastAPI, HTTPException, Request, responses
+from fastapi.middleware.cors import CORSMiddleware
+from llama_cpp import Llama
+
+print("Loading model...")
+llm = Llama(
+      model_path="/models/final-gemma2b_SA-Q5_K.gguf",
+      # n_gpu_layers=28, # Uncomment to use GPU acceleration
+      # seed=1337, # Uncomment to set a specific seed
+      # n_ctx=2048, # Uncomment to increase the context window
+)
+
+def ask(question, max_new_tokens=200):
+  output = llm(
+    question, # Prompt
+    max_tokens=max_new_tokens, # Generate up to 32 tokens, set to None to generate up to the end of the context window
+    stop=["\n"], # Stop generating just before the model would generate a new question
+    echo=False, # Echo the prompt back in the output
+    temperature=0.0,
+  )
+  return output
+
+def check_sentiment(text):
+  result = ask(f'Analyze the sentiment of the tweet enclosed in square brackets, determine if it is positive or negative, and return the answer as the corresponding sentiment label "positive" or  "negative" [{text}] =', max_new_tokens=3)
+  return result['choices'][0]['text'].strip()
+
+print("Testing model...")
+assert "positive" in check_sentiment("ดอกไม้ร้านนี้สวยจัง")
+print("Ready.")
+
+app = FastAPI(
+    title = "GemmaSA_2b",
+    description="A simple sentiment analysis API for the Thai language, powered by a finetuned version of Gemma-2b",
+    version="1.0.0",
+)
+
+origins = ["*"]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"]
+)
+
+@app.get('/')
+def docs():
+  "Redirects the user from the main page to the docs."
+  return responses.RedirectResponse('./docs')
+
+@app.get('/add/{a}/{b}')
+def add(a: int,b: int):
+    return a + b
+
+@app.get('/SA')
+def perform_sentiment_analysis(request: Request):
+    """Performs a sentiment analysis using a finetuned version of Gemma-7b"""
+    prompt = request.query_params.get('prompt')
+    if prompt:
+       try:
+        print(f"Checking sentiment for {prompt}")
+        result = check_sentiment(prompt)
+        print(f"Result: {result}")
+        return {'success': True, 'result': result}
+       except Exception as e:
+          return HTTPException(500, str(e))
+    else:
+       return HTTPException(400, "Request argument 'prompt' not provided.")

models/final-gemma2b_SA-Q5_K.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a138b3286615045657dc3e559ce97aed434e47bc8887113852e6281bdac9aed4
+size 1839868832

requirements.txt

@@ -0,0 +1,3 @@
+uvicorn[standard]
+fastapi
+llama-cpp-python