Update app.py

app.py

@@ -1,30 +1,33 @@
 import gradio as gr
-from transformers import pipeline
+from llama_cpp import Llama
 
-# 1. Load the AI Brain (GPT-2)
-# We do this OUTSIDE the function so it only loads once when the app starts
-print("Loading AI...")
-generator = pipeline('text-generation', model='gpt2')
+# 1. Load the Model
+# This automatically downloads the "DeepSeek-R1-Distill-Llama-8B" (GGUF version)
+# We use the Q4_K_M version because it fits in the FREE 16GB RAM tier.
+print("⏳ Downloading & Loading Model... (This takes 1-2 mins on first run)")
 
-# 2. Define the function
-def ask_brain(question):
-    # Ask the AI to generate text
-    result = generator(question, max_length=100, num_return_sequences=1)
-    
-    # Extract the text properly
-    answer = result[0]['generated_text']
-    
-    # Return the answer to the user
-    return answer
-
-# 3. Create the App Interface
-iface = gr.Interface(
-    fn=ask_brain, 
-    inputs="text", 
-    outputs="text",
-    title="My Free AI Model",
-    description="Ask me anything!"
+llm = Llama.from_pretrained(
+    repo_id="bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF",
+    filename="DeepSeek-R1-Distill-Llama-8B-Q4_K_M.gguf",
+    verbose=True,
+    n_ctx=4096  # Context window (memory of the conversation)
 )
 
-# 4. Launch the App
-iface.launch()
+# 2. Define the Chat Function
+def chat_with_deepseek(message, history):
+    # Format the prompt for DeepSeek
+    # It expects: User: <msg> \n Assistant:
+    prompt = f"User: {message}\nAssistant:"
+    
+    # Generate response
+    output = llm(
+        prompt, 
+        max_tokens=512,  # How long the answer can be
+        stop=["User:", "\n\n"], # Stop it from talking to itself
+        echo=False
+    )
+    return output['choices'][0]['text']
+
+# 3. Launch the Chat Interface
+# We use ChatInterface because it handles the UI automatically
+gr.ChatInterface(chat_with_deepseek).launch()