Load models using dropdown

app.py

@@ -1,71 +1,64 @@
-import os
-import subprocess
-
-def install(package):
-    subprocess.check_call([os.sys.executable, "-m", "pip", "install", package])
-
-install("transformers")
-
 import os
 import torch
 import gradio as gr
 import spaces
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Use a global variable to hold the current model and tokenizer
+current_model = None
+current_tokenizer = None
+
+def load_model_on_selection(model_name, progress=gr.Progress(track_tqdm=False)):
+    global current_model, current_tokenizer
+    token = os.getenv("HF_TOKEN")
+
+    progress(0, desc="Loading tokenizer...")
+    current_tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
 
-# Global cache for loaded models
-model_cache = {}
+    progress(0.5, desc="Loading model...")
+    current_model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        use_auth_token=token
+    )
 
-# Load a model with progress bar
-def load_model(model_name, progress=gr.Progress(track_tqdm=False)):
-    if model_name not in model_cache:
-        token = os.getenv("HF_TOKEN")
-        progress(0, desc="Loading tokenizer...")
-        tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=token)
-        progress(0.5, desc="Loading model...")
-        model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype=torch.float16,
-            device_map="auto",
-            use_auth_token=token
-        )
-        model_cache[model_name] = (tokenizer, model)
-        progress(1, desc="Model ready.")
-        return f"{model_name} loaded and ready!"
-    else:
-        return f"{model_name} already loaded."
+    progress(1, desc="Model ready.")
+    return f"{model_name} loaded and ready!"
 
-# Inference function using GPU
 @spaces.GPU
-def generate_text(model_name, prompt):
-    tokenizer, model = model_cache[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)
+def generate_text(prompt):
+    global current_model, current_tokenizer
+    if current_model is None or current_tokenizer is None:
+        return "⚠️ No model loaded yet. Please select a model first."
+    
+    inputs = current_tokenizer(prompt, return_tensors="pt").to(current_model.device)
+    outputs = current_model.generate(**inputs, max_new_tokens=256)
+    return current_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Available models
+# Model options
 model_choices = [
     "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
     "meta-llama/Llama-3.2-3B-Instruct",
     "google/gemma-7b"
 ]
 
-# Gradio Interface
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## Clinical Text Analysis with LLMs (LLaMA, DeepSeek, Gemma)")
-    
-    with gr.Row():
-        model_selector = gr.Dropdown(choices=model_choices, label="Select Model")
-        model_status = gr.Textbox(label="Model Status", interactive=False)
+    gr.Markdown("## Clinical Text Testing with LLaMA, DeepSeek, and Gemma")
+
+    model_selector = gr.Dropdown(choices=model_choices, label="Select Model")
+    model_status = gr.Textbox(label="Model Status", interactive=False)
 
     input_text = gr.Textbox(label="Input Clinical Text")
     output_text = gr.Textbox(label="Generated Output")
-    
-    analyze_button = gr.Button("Analyze")
 
-    # Load model when changed
-    model_selector.change(fn=load_model, inputs=model_selector, outputs=model_status)
-    
-    # Generate output
-    analyze_button.click(fn=generate_text, inputs=[model_selector, input_text], outputs=output_text)
+    generate_btn = gr.Button("Generate")
+
+    # Load model on dropdown change
+    model_selector.change(fn=load_model_on_selection, inputs=model_selector, outputs=model_status)
+
+    # Generate with current model
+    generate_btn.click(fn=generate_text, inputs=input_text, outputs=output_text)
 
 demo.launch()