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@@ -1,33 +1,30 @@
-FROM python:3.10-slim
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    build-essential \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+## Use the official Python 3.9 image
+FROM python:3.9
 
+## Set the working directory to /code
 WORKDIR /code
 
-# Copy requirements file
+## Copy the requirements.txt file into the container at /code
 COPY ./requirements.txt /code/requirements.txt
 
-# Upgrade pip and install requirements
-RUN pip install --no-cache-dir --upgrade pip && \
-    pip install --no-cache-dir -r /code/requirements.txt
+## Install the requirements from requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+## Set up a new user named "user"
+RUN useradd user
 
-# Create and use non-root user
-RUN useradd -m user
+## Switch to the "user" user
 USER user
 
-# Set environment variables
+## Set home to the user's home directory
 ENV HOME=/home/user \
-    PATH=/home/user/.local/bin:$PATH \
-    PYTHONUNBUFFERED=1
+    PATH=/home/user/.local/bin:$PATH
 
+## Set the working directory to the user's home directory
 WORKDIR $HOME/app
 
-# Copy application code
+## Copy the current directory contents into the container at $HOME/app and set the owner to "user"
 COPY --chown=user . $HOME/app
 
-# Run the application
-CMD ["python", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
+## Start the FASTAPI app on port 7860
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -1,74 +1,20 @@
-import os
-import logging
-from fastapi import FastAPI, HTTPException
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
-from peft import PeftModel, PeftConfig
+from fastapi import FastAPI
+from transformers import pipeline
 
-# Set up logging
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Initialize FastAPI app
+# Create a new FASTAPI app instance
 app = FastAPI()
 
-# Global variables for model, tokenizer, and pipeline
-model = None
-tokenizer = None
-pipe = None
-
-@app.on_event("startup")
-async def load_model():
-    global model, tokenizer, pipe
-    
-    try:
-        # Get Hugging Face token from environment variable
-        hf_token = os.environ.get("HUGGINGFACE_TOKEN")
-        
-        logger.info("Loading PEFT configuration...")
-        config = PeftConfig.from_pretrained("frankmorales2020/Mistral-7B-text-to-sql-flash-attention-2-dataeval")
-        
-        # Debugging: Print the configuration
-        logger.info(f"Configuration: {config}")
-
-        logger.info("Loading base model...")
-        base_model = AutoModelForCausalLM.from_pretrained(
-            "mistralai/Mistral-7B-Instruct-v0.3", 
-            use_auth_token=hf_token
-        )
-        
-        logger.info("Loading PEFT model...")
-        model = PeftModel.from_pretrained(base_model, "frankmorales2020/Mistral-7B-text-to-sql-flash-attention-2-dataeval")
-
-        logger.info("Loading tokenizer...")
-        tokenizer = AutoTokenizer.from_pretrained(
-            "mistralai/Mistral-7B-Instruct-v0.3", 
-            use_auth_token=hf_token
-        )
-
-        logger.info("Creating pipeline...")
-        pipe = pipeline("text2text-generation", model=model, tokenizer=tokenizer)
-        
-        logger.info("Model, tokenizer, and pipeline loaded successfully.")
-    except Exception as e:
-        logger.error(f"Error loading model or creating pipeline: {e}")
-        raise
+# Initialize the text generation pipeline
+pipe = pipeline("text2text-generation", model="juierror/text-to-sql-with-table-schema")
 
 @app.get("/")
 def home():
     return {"message": "Hello World"}
 
+# Define a function to handle the GET request at '/generate'
 @app.get("/generate")
-async def generate(text: str):
-    if not pipe:
-        raise HTTPException(status_code=503, detail="Model not loaded")
-    
-    try:
-        output = pipe(text, max_length=100, num_return_sequences=1)
-        return {"output": output[0]['generated_text']}
-    except Exception as e:
-        logger.error(f"Error during text generation: {e}")
-        raise HTTPException(status_code=500, detail=f"Error during text generation: {str(e)}")
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+def generate(text: str):
+    # Use the pipeline to generate text from the given input text
+    output = pipe(text)
+    # Return the generated text in JSON response
+    return {"output": output[0]['generated_text']}

requirements.txt

@@ -1,9 +1,9 @@
-fastapi==0.103.0
+
+requests==2.27.*
 uvicorn[standard]==0.17.*
-torch>=1.13.0
-transformers>=4.34.0,<4.35.0
-numpy<2
-peft>=0.6.0,<0.7.0
-accelerate>=0.24.1,<0.25.0
-huggingface_hub>=0.16.4,<0.18.0
-tokenizers>=0.14.0,<0.15.0
+sentencepiece==0.1.*
+torch==1.11.*
+
+
+fastapi==0.74.*
+transformers==4.*