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Modelfile

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+# This tells Ollama to pull your model directly from Hugging Face Hub
+FROM ./krishna_choudhary/AI_Assistant_Chatbot:latest
+
+TEMPLATE """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+
+{{ .System }}<|eot_id|><|start_header_id|>user<|end_header_id|>
+
+{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+
+"""
+
+PARAMETER stop "<|start_header_id|>"
+PARAMETER stop "<|end_header_id|>"
+PARAMETER stop "<|eot_id|>"
+PARAMETER stop "<|end_of_text|>"
+
+# This single line is crucial for catching all reserved tokens
+PARAMETER stop "<|reserved_special_token_"
+
+PARAMETER stop "\n<|start_header_id|>user"
+PARAMETER stop "\n<|start_header_id|>assistant"
+PARAMETER stop "user"
+PARAMETER stop "assistant"
+
+SYSTEM """You are a GUVI chatbot, acting as customer care. You will solve customer queries with a polite and helpful tone.
+You are available 24/7 to assist.
+If the customer provides any numbers (e.g., in an order ID, reference number, or contact number), you must refer to them with a general term like "your order number," "the reference number provided," or "the contact number" instead of printing the exact digits.
+Crucially, **do NOT output any internal thoughts, debugging information, or special tokens**. Ensure your response ends cleanly, specifically avoiding tokens such as <|reserved_special_token_XXX|> (where XXX is any number).
+"""

start.sh

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+#!/bin/bash
+
+# Do NOT export HOME here. Let the system manage HOME for the 'appuser'
+# (which will be /home/appuser due to the Dockerfile change).
+# Ollama will try to write to /home/appuser/.ollama if it falls back on HOME.
+
+# Define OLLAMA_HOME explicitly for the runtime environment in start.sh.
+# This ensures Ollama uses /app/.ollama for its primary data, which is within the /app
+# directory owned by 'appuser'.
+export OLLAMA_HOME="/app/.ollama"
+
+# Create the Ollama home directory if it doesn't exist.
+# The Dockerfile's chown ensures 'appuser' owns /app, so 'appuser' can
+# create this directory without permission issues.
+mkdir -p "${OLLAMA_HOME}"
+
+echo "Starting Ollama server..."
+# Start Ollama server in the background.
+# Redirect its output to a log file within OLLAMA_HOME, and also to stdout (Hugging Face Space logs).
+/usr/local/bin/ollama serve > "${OLLAMA_HOME}/ollama.log" 2>&1 &
+
+# Give Ollama a moment to write logs before tailing.
+# This helps ensure `tail -f` has content to display immediately.
+sleep 2
+
+# Tail the Ollama log file in the background so its contents appear in the Space logs.
+# This is crucial for debugging Ollama's internal startup process.
+tail -f "${OLLAMA_HOME}/ollama.log" &
+
+echo "Waiting for Ollama server to start..."
+attempt_num=0
+max_attempts=60 # Increased timeout to 60 seconds (60 attempts * 1 second sleep)
+while ! curl -s http://localhost:11434/api/tags > /dev/null; do
+    if [ $attempt_num -ge $max_attempts ]; then
+        echo "Error: Ollama server failed to start after multiple attempts."
+        echo "Ollama logs:"
+        # Print the full Ollama log if it fails to start for final debugging.
+        cat "${OLLAMA_HOME}/ollama.log"
+        exit 1 # Exit with an error to signal failure to Hugging Face Spaces.
+    fi
+    echo "Ollama server not yet ready, waiting... ($((attempt_num + 1))/$max_attempts)"
+    sleep 1
+    attempt_num=$((attempt_num + 1))
+done
+
+echo "Ollama server is ready!"
+echo "Starting Streamlit app..."
+
+# Run the Streamlit application.
+# --server.port and --server.address are essential for Docker environments
+# to ensure Streamlit listens correctly and is accessible via Hugging Face Spaces.
+streamlit run src/streamlit_app.py --server.port 8501 --server.address 0.0.0.0