Update app.py

app.py

@@ -1,42 +1,67 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 
-import torch 
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 
-
-torch.random.manual_seed(0) 
-model = AutoModelForCausalLM.from_pretrained( 
-    "AdnanRiaz107/CodePhi-3-Mini-1K4bit",  
-    device_map="cuda",  
-    torch_dtype="auto",  
-    trust_remote_code=True,  
-    attn_implementation='eager', 
-) 
-
-tokenizer = AutoTokenizer.from_pretrained("AdnanRiaz107/CodePhi-3-Mini-1K4bit") 
-
-messages = [ 
-    {"role": "system", "content": "You are a helpful AI assistant."}, 
-    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}, 
-    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."}, 
-    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"}, 
-] 
-
-pipe = pipeline( 
-    "text-generation", 
-    model=model, 
-    tokenizer=tokenizer, 
-) 
-
-generation_args = { 
-    "max_new_tokens": 500, 
-    "return_full_text": False, 
-    "temperature": 0.0, 
-    "do_sample": False, 
-} 
-
-output = pipe(messages, **generation_args) 
-print(output[0]['generated_text'])
+# Set the random seed for reproducibility
+torch.random.manual_seed(0)
+
+# Load the model without specifying 'device_map' for CPU usage
+model = AutoModelForCausalLM.from_pretrained(
+    "AdnanRiaz107/CodePhi-3-Mini-1K4bit",
+    torch_dtype="auto",  # Use auto for dtype selection
+    trust_remote_code=True,
+    attn_implementation='eager',  # Keep this if you want to use 'eager'
+)
+
+# Load the tokenizer
+tokenizer = AutoTokenizer.from_pretrained("AdnanRiaz107/CodePhi-3-Mini-1K4bit")
+
+# Create a text generation pipeline
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+
+# Generation arguments
+generation_args = {
+    "max_new_tokens": 500,
+    "return_full_text": False,
+    "temperature": 0.0,
+    "do_sample": False,
+}
+
+# Gradio interface function
+def generate_response(input_text):
+    # Prepare the input for the model
+    messages = [{"role": "user", "content": input_text}]
+    
+    # Generate output
+    output = pipe(messages, **generation_args)
+    return output[0]['generated_text']
+
+# Create Gradio demo interface
+demo = gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(
+        lines=2,
+        placeholder="Enter your question here...",
+        label="Your Input",
+    ),
+    outputs=gr.Textbox(
+        label="Model Response",
+        placeholder="Response will be displayed here...",
+    ),
+    title="AI Assistant for Python Code Generation",
+    description="Ask any question or request information, and the AI assistant will provide a response. Try asking about recipes, solving equations, or general inquiries.",
+    examples=[
+        ["Can you provide ways to eat combinations of bananas and dragonfruits?"],
+        ["What about solving the equation 2x + 3 = 7?"],
+        ["Tell me about the history of the internet."],
+    ],
+    theme="default"  # You can change the theme to "compact", "default", "huggingface", etc.
+)
 
 if __name__ == "__main__":
     demo.launch()