first commit

app.py

@@ -1,7 +1,44 @@
 import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+import spaces
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Dictionary to store loaded models and tokenizers
+loaded_models = {}
+
+def load_model(model_name):
+    """Load the model and tokenizer if not already loaded."""
+    if model_name not in loaded_models:
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name, torch_dtype=torch.float16, device_map="auto"
+        )
+        loaded_models[model_name] = (tokenizer, model)
+    return loaded_models[model_name]
+
+@spaces.GPU
+def generate_text(model_name, prompt):
+    """Generate text using the selected model."""
+    tokenizer, model = load_model(model_name)
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# List of models to choose from
+model_choices = [
+    "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
+    "meta-llama/Llama-3.2-3B-Instruct",
+    "google/gemma-7b"
+]
+
+# Gradio interface setup
+with gr.Blocks() as demo:
+    gr.Markdown("## Clinical Text Analysis with Multiple Models")
+    model_selector = gr.Dropdown(choices=model_choices, label="Select Model")
+    input_text = gr.Textbox(label="Input Clinical Text")
+    output_text = gr.Textbox(label="Generated Output")
+    analyze_button = gr.Button("Analyze")
+
+    analyze_button.click(fn=generate_text, inputs=[model_selector, input_text], outputs=output_text)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 demo.launch()