Create app.py

app.py

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+import gradio as gr
+import time
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("IEEEVITPune-AI-Team/ChatbotAlpha0.7")
+model = AutoModelForCausalLM.from_pretrained("IEEEVITPune-AI-Team/ChatbotAlpha0.7")
+
+# Define function to generate response
+def generate_response(message, history, system_prompt, tokens):
+    # Concatenate system prompt and user message
+    input_text = f"{system_prompt} {message}"
+    # Tokenize input text
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    # Generate response
+    output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    return response
+
+# Define Gradio interface
+with gr.Blocks() as demo:
+    system_prompt = gr.Textbox("You are helpful AI.", label="System Prompt")
+    slider = gr.Slider(10, 100, render=False, label="Number of Tokens")
+    gr.ChatInterface(
+        generate_response,
+        inputs=["text", "text", system_prompt, slider],
+        outputs="text"
+    )
+
+# Launch Gradio interface
+demo.launch()