Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import openai
+import torch
+
+# Load Llama model (GPU-optimized)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+llama_tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
+llama_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-7b-chat-hf", 
+    device_map="auto"
+)
+
+# OpenAI GPT Model API Key (Replace with your API key)
+openai.api_key = "YOUR_OPENAI_API_KEY"
+
+# Function to query Llama
+def query_llama(prompt):
+    inputs = llama_tokenizer(prompt, return_tensors="pt", truncation=True, max_length=128).to(device)
+    outputs = llama_model.generate(inputs.input_ids, max_length=150)
+    response = llama_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Function to query GPT
+def query_gpt(prompt):
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=prompt,
+        max_tokens=150
+    )
+    return response['choices'][0]['text'].strip()
+
+# Function to compare models
+def compare_models(prompt, models):
+    responses = {}
+    if "Llama" in models:
+        responses["Llama"] = query_llama(prompt)
+    if "GPT" in models:
+        responses["GPT"] = query_gpt(prompt)
+    return responses
+
+# Gradio Interface
+def gradio_app():
+    with gr.Blocks() as app:
+        gr.Markdown("# AI Model Comparison Tool 🚀")
+        with gr.Row():
+            prompt_input = gr.Textbox(label="Enter your prompt", placeholder="Ask something...")
+        with gr.Row():
+            model_selector = gr.CheckboxGroup(
+                ["Llama", "GPT"], 
+                label="Select Models to Compare", 
+                value=["Llama", "GPT"]
+            )
+        with gr.Row():
+            output_boxes = gr.JSON(label="Model Responses")
+        with gr.Row():
+            compare_button = gr.Button("Compare Models")
+        compare_button.click(compare_models, inputs=[prompt_input, model_selector], outputs=[output_boxes])
+    return app
+
+if __name__ == "__main__":
+    gradio_app().launch()