Update Dockerfile

Dockerfile

@@ -1,22 +1,17 @@
-# Use a clean, lightweight Python image
-FROM python:3.10-slim-bookworm
+# Use Full Python 3.10
+FROM python:3.10
 
 WORKDIR /app
 
-# 1. Install Runtime Dependencies (No compilers needed)
-# libopenblas-0: Required for the pre-built AI engine to run
-# poppler-utils: Required for PDF
-# libgl1: Required for Vision
+# 1. Install System Dependencies
 RUN apt-get update && apt-get install -y \
-    libopenblas-0 \
     poppler-utils \
     libgl1 \
     libglib2.0-0 \
     && rm -rf /var/lib/apt/lists/*
 
-# 2. Install Python Packages (STRICT PRE-BUILT MODE)
-# --prefer-binary: Tells pip to use the wheel even if it's slightly older
-# --extra-index-url: Points to the specific CPU-optimized storage
+# 2. Install Python Libraries (Pre-built)
+# Llava is fully supported by the standard CPU wheel
 RUN pip install --no-cache-dir --prefer-binary \
     "llama-cpp-python>=0.3.1" \
     --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu
@@ -29,14 +24,14 @@ RUN pip install --no-cache-dir \
     fastapi \
     pillow
 
-# 3. Download Model (Bartowski's GGUF)
-RUN huggingface-cli download bartowski/Qwen2-VL-7B-Instruct-GGUF --include "Qwen2-VL-7B-Instruct-Q4_K_M.gguf" --local-dir /app/model
+# 3. Download Model (Llava v1.6 Mistral)
+# Repo: cjpais/llava-1.6-mistral-7b-gguf
+# We download the Model (Q4_K_M) and the Projector (mmproj)
+RUN huggingface-cli download cjpais/llava-1.6-mistral-7b-gguf --include "llava-v1.6-mistral-7b.Q4_K_M.gguf" --local-dir /app/model
+RUN huggingface-cli download cjpais/llava-1.6-mistral-7b-gguf --include "mmproj-model-f16.gguf" --local-dir /app/model
 
-# 4. Download Vision Projector
-RUN huggingface-cli download bartowski/Qwen2-VL-7B-Instruct-GGUF --include "mmproj-Qwen2-VL-7B-Instruct-f16.gguf" --local-dir /app/model
-
-# 5. Copy App Code
+# 4. Copy Code
 COPY app.py .
 
-# 6. Run Server
+# 5. Run Server
 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]