add initial project setup with Gradio chat interface and model integration

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.python-version

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+3.13

app.py

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+import gradio as gr
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+import os
+
+# Configuration
+MODEL_REPO = "SaiBon99/llama-finetuned-gguf"
+MODEL_FILE = "model.gguf"
+
+print(f"Downloading model from {MODEL_REPO}...")
+
+try:
+    # Download the GGUF model file from HuggingFace
+    model_path = hf_hub_download(
+        repo_id=MODEL_REPO,
+        filename=MODEL_FILE,
+        repo_type="model"
+    )
+    print(f"Model downloaded to: {model_path}")
+
+    # Load the model with llama-cpp-python
+    print("Loading model into memory...")
+    llm = Llama(
+        model_path=model_path,
+        n_ctx=2048,  # Context window
+        n_threads=4,  # Number of CPU threads
+        n_gpu_layers=0,  # Set to 0 for CPU, increase for GPU
+        verbose=False,
+    )
+    print("Model loaded successfully!")
+
+except Exception as e:
+    print(f"Error loading model: {e}")
+    llm = None
+
+def chat(message, history):
+    """
+    Chat function that takes a message and chat history,
+    and returns the model's response.
+    """
+    if llm is None:
+        return "Error: Model failed to load. Please check the logs."
+
+    # Build the conversation in Llama 3 chat format
+    conversation = "<|begin_of_text|>"
+
+    if history:
+        for user_msg, bot_msg in history:
+            conversation += f"<|start_header_id|>user<|end_header_id|>\n\n{user_msg}<|eot_id|>"
+            conversation += f"<|start_header_id|>assistant<|end_header_id|>\n\n{bot_msg}<|eot_id|>"
+
+    # Add the current message
+    conversation += f"<|start_header_id|>user<|end_header_id|>\n\n{message}<|eot_id|>"
+    conversation += "<|start_header_id|>assistant<|end_header_id|>\n\n"
+
+    # Generate response
+    try:
+        response = llm(
+            conversation,
+            max_tokens=512,
+            temperature=0.7,
+            top_p=0.9,
+            repeat_penalty=1.1,
+            stop=["<|eot_id|>", "<|start_header_id|>"],
+            echo=False
+        )
+
+        return response['choices'][0]['text'].strip()
+
+    except Exception as e:
+        return f"Error generating response: {str(e)}"
+
+# Create Gradio ChatInterface
+demo = gr.ChatInterface(
+    fn=chat,
+    title="Llama 3.2 3B Fine-tuned Chat",
+    description=f"Chat with Llama 3.2 3B fine-tuned on FineTome-100k dataset (GGUF format)\n\nModel: `{MODEL_REPO}`",
+    examples=[
+        "Explain what boolean operators are and how they work in programming.",
+        "What is the difference between short-circuit evaluation and normal evaluation?",
+        "Write a Python function to check if a number is prime.",
+        "Explain the concept of operator precedence with examples.",
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

pyproject.toml

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+[project]
+name = "iris"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "gradio>=6.0.1",
+]