Adding Docker

Dockerfile

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+FROM python:3.10-slim-buster
+
+# Set the working directory
+WORKDIR /app
+
+# Copy requirements file
+COPY requirements.txt requirements.txt
+
+# Update package list and install necessary packages in a single step
+RUN apt-get update && apt-get install -y \
+    curl \
+    build-essential \
+    libffi-dev \
+    cmake \
+    libcurl4-openssl-dev \
+    tini && \
+    apt-get clean
+
+# Upgrade pip and install dependencies
+RUN python -m venv venv && \
+    . /app/venv/bin/activate && \
+    pip install --upgrade pip && \
+    pip install --no-cache-dir -r requirements.txt
+
+# Install Ollama
+RUN curl https://ollama.ai/install.sh | sh
+
+# Create the directory and give appropriate permissions
+RUN mkdir -p /.ollama && chmod 777 /.ollama
+
+# Ensure Ollama binary is in the PATH
+ENV PATH="/app/venv/bin:/root/.ollama/bin:$PATH"
+
+# Expose the server port
+EXPOSE 7860
+EXPOSE 11434
+EXPOSE 1338
+EXPOSE 8501
+# Copy the entry point script
+COPY entrypoint.sh /entrypoint.sh
+RUN chmod +x /entrypoint.sh
+
+# Set the model as an environment variable (this can be overridden)
+ENV model="default_model"
+
+# Copy the entire application
+COPY . .
+
+# Set proper permissions for the translations directory
+RUN chmod -R 777 translations
+
+# Copy the startup script and make it executable
+#COPY start.sh .
+#RUN chmod +x start.sh
+
+# Define the command to run the application
+# Set the entry point script as the default command
+ENTRYPOINT ["/entrypoint.sh"]

entrypoint.sh

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+#!/bin/bash
+
+# Source the virtual environment
+source /app/venv/bin/activate
+
+# Starting server
+echo "Starting Ollama server"
+ollama serve &
+sleep 1
+
+# Try to get the model environment variable
+if [ -n "${MODEL}" ]; then
+  # Split the MODEL variable into an array
+  IFS=',' read -ra MODELS <<< "${MODEL}"
+else
+  # Use the default list of models
+  MODELS=(llama3 ) #gemma:2b phi3 mistral
+fi
+
+
+# Splitting the models by comma and pulling each
+#IFS=',' read -ra MODELS <<< "$model"
+for m in "${MODELS[@]}"; do
+    echo "Pulling $m"
+    ollama pull "$m"
+    sleep 5
+done
+
+
+ollama create aws-path-learning -f ./Modelfile
+
+# Run the Python application
+exec python streamlit run ./src/app.py
+
+# Keep the script running to prevent the container from exiting
+#wait

requirements.txt

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+websocket-client
+python-dotenv
+requests
+tls-client
+pypasser
+names
+colorama
+curl_cffi
+aiohttp
+flask
+flask_cors
+flask-babel
+streamlit
+selenium
+fake-useragent
+twocaptcha
+pydantic
+pymailtm
+Levenshtein
+retrying
+numpy>=1.22.2 # pinned to avoid a vulnerability
+tornado>=6.3.2 # pinned to avoid a vulnerability
+PyExecJS
+openai==0.28.1
+g4f
+langchain-community
+ollama