update codebase

Dockerfile

@@ -0,0 +1,43 @@
+# Use the custom FastAPI image
+# FROM tiangolo/uvicorn-gunicorn-fastapi:python3.9
+# Use ubuntu as base image
+FROM ubuntu:20.04
+
+# Avoid prompts from apt
+ENV DEBIAN_FRONTEND=noninteractive
+
+# Update apt repositories and install Python and Pip
+RUN apt-get update && \
+    apt-get install -y python3-pip python3-dev
+
+# Check if the symbolic links already exist and create them if they don't
+RUN if [ ! -e /usr/bin/python ]; then ln -s /usr/bin/python3 /usr/bin/python; fi && \
+    if [ ! -e /usr/bin/pip ]; then ln -s /usr/bin/pip3 /usr/bin/pip; fi
+
+# Set default values for environment variables
+ENV OPENAI_ORG_ID=default_org_id
+ENV OPENAI_API_KEY=default_api_key
+ENV HUGGINGFACE_API_TOKEN=default_huggingface_token
+
+# Set environment variables for Matplotlib and Fontconfig
+ENV MPLCONFIGDIR=/app/matplotlib_cache
+ENV FONTCONFIG_PATH=/app/fontconfig
+
+# Create the directories for Matplotlib cache and Fontconfig
+RUN mkdir -p /app/matplotlib_cache /app/fontconfig && \
+    chmod -R 777 /app/matplotlib_cache /app/fontconfig
+
+# Create a writable directory for Fontconfig cache
+RUN mkdir -p /app/fontconfig_cache && chmod -R 777 /app/fontconfig_cache
+
+# Set the environment variable so Fontconfig uses the writable directory
+ENV FONTCONFIG_PATH=/app/fontconfig_cache
+
+# Copy the requirements file and install dependencies
+COPY ./requirements.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy your application source code and script
+COPY ./api /app
+
+CMD ["uvicorn", "app.app.main:app", "--host", "0.0.0.0", "--port", "7860"]

ai/chatbot.py

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+import os
+from openai import OpenAI
+import requests
+
+API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
+
+API_URL = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-chat-hf"
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+
+client = OpenAI(
+    organization=os.getenv("OPENAI_ORG_ID"), api_key=os.getenv("OPENAI_API_KEY")
+)
+
+
+def gpt_chatbot(user_request: str):
+    completion = client.chat.completions.create(
+        model="gpt-4o-mini",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": user_request},
+        ],
+    )
+
+    return completion.choices[0].message.content    
+
+
+def llama_chatbot(user_request: str):
+    response = requests.post(API_URL, headers=headers, json={"inputs": user_request})
+
+    return response.json()[0]["generated_text"]