Create app.py

app.py

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+
+import os
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# Function to load model and tokenizer based on selection
+def load_model(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
+
+# Define the function to generate a response with adjustable parameters and model-specific adjustments
+def generate_response(prompt, model_name, persona="I am a helpful assistant.", temperature=0.7, top_p=0.9, repetition_penalty=1.2, max_length=70):
+    # Load the chosen model and tokenizer
+    tokenizer, model = load_model(model_name)
+
+    # Adjust the prompt format for DialoGPT
+    if model_name == "microsoft/DialoGPT-small":
+        full_prompt = f"User: {prompt}\nBot:"  # Structure as a conversation
+    else:
+        full_prompt = f"{persona}: {prompt}"  # Standard format for other models
+
+    # Tokenize and generate response
+    inputs = tokenizer(full_prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs.input_ids,
+        max_length=max_length,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
+
+    # Trim the prompt if it appears in the response
+    if model_name == "microsoft/DialoGPT-small":
+        response_without_prompt = response.split("Bot:", 1)[-1].strip()
+    else:
+        response_without_prompt = response.split(":", 1)[-1].strip()
+
+    return response_without_prompt if response_without_prompt else "I'm not sure how to respond to that."
+
+# Define Gradio interface function with model selection
+def chat_interface(user_input, model_choice, persona="I am a helpful assistant", temperature=0.7, top_p=0.9, repetition_penalty=1.2, max_length=50):
+    return generate_response(user_input, model_choice, persona, temperature, top_p, repetition_penalty, max_length)
+
+# Set up Gradio interface with model selection and parameter sliders
+interface = gr.Interface(
+    fn=chat_interface,
+    inputs=[
+        gr.Textbox(label="User Input"),
+        gr.Dropdown(choices=["distilgpt2", "gpt2", "microsoft/DialoGPT-small"], label="Model Choice", value="distilgpt2"),
+        gr.Textbox(label="Persona", value="You are a helpful assistant."),
+        gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7, step=0.1),
+        gr.Slider(label="Top-p (Nucleus Sampling)", minimum=0.1, maximum=1.0, value=0.9, step=0.1),
+        gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.2, step=0.1),
+        gr.Slider(label="Max Length", minimum=10, maximum=100, value=50, step=5)
+    ],
+    outputs="text",
+    title="Interactive Chatbot with Model Comparison",
+    description="Chat with the bot! Select a model and adjust parameters to see how they affect the response."
+)
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    interface.launch()