Create app.py

app.py

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+import gradio as gr
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import spaces
+
+# Check if we're running in a Hugging Face Space with zero GPU constraints
+IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
+IS_SPACE = os.environ.get("SPACE_ID", None) is not None
+
+# Determine device (set to CPU for zero-GPU)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load model and tokenizer
+model_name = "linjc16/Panacea-7B-Chat"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
+model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Define prompt structure
+@spaces.GPU  # This will handle spaces for either GPU or CPU as available
+def generate_response(system_instruction, user_input):
+    # Format the prompt with the system instruction and user input
+    prompt = f"{system_instruction}\n\nUser: {user_input}\nBot:"
+    
+    # Tokenize and prepare inputs
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    # Generate model response
+    with torch.no_grad():
+        outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True)
+    
+    # Decode response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True).split("Bot:")[-1].strip()
+
+    return response  # Return only bot's response for display in the right column
+
+# Set up Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Clinical Trial Chatbot")
+
+    with gr.Row():
+        # Left column for inputs
+        with gr.Column():
+            system_instruction = gr.Textbox(
+                placeholder="Enter system instruction here...", label="System Instruction")
+            user_input = gr.Textbox(
+                placeholder="Type your message here...", label="Your Message")
+            submit_btn = gr.Button("Submit")
+
+        # Right column for displaying bot's response
+        with gr.Column():
+            response_display = gr.Textbox(
+                label="Bot Response", interactive=False, placeholder="Response will appear here.")
+
+    # Link the submit button to the generate_response function
+    submit_btn.click(generate_response, [system_instruction, user_input], response_display)
+
+# Launch the app
+demo.launch(share=True)