Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# --- 1. Model Initialization (Loads once when the app starts) ---
+print("🔄 Loading the AI model... This will take a moment on the first run.")
+# Model name from Hugging Face Hub
+model_name = "Qwen/Qwen2.5-1.5B-Instruct"
+
+# Load the tokenizer and model
+# We explicitly set `device_map="cpu"` to ensure it runs on the free CPU hardware.
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float32,  # Use float32 for CPU stability
+    device_map="cpu",
+    trust_remote_code=True
+)
+print("✅ AI Model loaded and ready!")
+
+# --- 2. The Core AI Function ---
+def chat_with_ai(message, history):
+    """
+    Takes the user's message and chat history, generates a response from the AI model.
+    """
+    # Construct the conversation prompt. The model expects a specific chat format.
+    # Here we build a simple prompt with the conversation history.
+    prompt = ""
+    for user_msg, bot_msg in history:
+        prompt += f"<|im_start|>user\n{user_msg}<|im_end|>\n"
+        prompt += f"<|im_start|>assistant\n{bot_msg}<|im_end|>\n"
+    # Add the current user message
+    prompt += f"<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n"
+
+    # Tokenize the input and generate a response
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():  # Disable gradient calculation for faster inference
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=512,        # Maximum length of the new response
+            temperature=0.7,           # Controls randomness (lower = more deterministic)
+            do_sample=True,            # Enable sampling for more creative responses
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    # Decode only the newly generated tokens (skip the input prompt)
+    generated_tokens = outputs[0][inputs['input_ids'].shape[1]:]
+    response = tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+    return response.strip()  # Return the clean response
+
+# --- 3. Gradio Interface Setup ---
+# gr.ChatInterface provides a perfect, ready-made UI for chatbots.
+demo = gr.ChatInterface(
+    fn=chat_with_ai,
+    title="🤖 Free AI Assistant on Hugging Face Spaces",
+    description="Ask me anything! I'm running entirely on a free CPU instance. Be patient, I'm thinking as fast as I can.",
+    theme="soft",
+    examples=["What is the capital of France?", "Explain quantum computing in simple terms.", "Write a short poem about coding."]
+)
+
+# --- 4. Launch the App ---
+if __name__ == "__main__":
+    demo.launch()