Added Ollama server files

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
+# 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=(llava llama3.2-vision)
+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
+
+
+# Run the Python application
+exec python ./src/run1.py

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
+ipykernel
+notebook
+jupyter
+g4f
+langchain-community
+ollama
+fastapi
+uvicorn

src/run1.py

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+from fastapi import FastAPI
+import requests
+
+URL = "http://localhost:11434/api/chat"
+headers = {"Content-Type": "application/json"}
+
+app = FastAPI()
+
+@app.get("/")
+def read_root():
+    return {"message": "hello world"}
+
+@app.post("/api/chat")
+def get_chat_response(body: dict):
+    print(f"Received body: {body}")
+    try:
+        response = requests.post(url=URL, headers=headers, json=body, timeout=600)
+        return response.json()
+    except ConnectionRefusedError as error:
+        return {"error": f"Connection refused from backend with error: {error}"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)