Update app.py

app.py

@@ -1,35 +1,26 @@
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer
+import transformers
+import torch
 
-# Load your text generation model from Hugging Face using its identifier
-model_identifier = "curiouscurrent/omnicode"
-model = AutoModelForCausalLM.from_pretrained(model_identifier)
-tokenizer = AutoTokenizer.from_pretrained(model_identifier)
+model = "codellama/CodeLlama-7b-hf"
 
-history = []
+tokenizer = AutoTokenizer.from_pretrained(model)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
 
-def generate_response(prompt):
-    history.append(prompt)
-    final_prompt = "\n".join(history)
-    
-    # Tokenize input prompt
-    input_ids = tokenizer.encode(final_prompt, return_tensors="pt", max_length=512, truncation=True)
-    
-    # Generate response
-    output_ids = model.generate(input_ids, max_length=100, num_return_sequences=1)
-    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-    
-    return response
-
-# Create Gradio interface
-input_prompt = gr.inputs.Textbox(lines=4, label="Input Prompt")
-output_text = gr.outputs.Textbox(label="Response")
-
-gr.Interface(
-    generate_response,
-    inputs=input_prompt,
-    outputs=output_text,
-    title="OmniCode",
-    description="Multi programming coding assistant",
-    theme="compact"
-).launch()
+sequences = pipeline(
+    'import socket\n\ndef ping_exponential_backoff(host: str):',
+    do_sample=True,
+    top_k=10,
+    temperature=0.1,
+    top_p=0.95,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id,
+    max_length=200,
+)
+for seq in sequences:
+    print(f"Result: {seq['generated_text']}")