test

app.py

@@ -1,58 +1,75 @@
 import gradio as gr
+from transformers import pipeline
 
-theme = gr.themes.Soft()
+# Initialize a dictionary of models for each strategy
+models = {
+    "GPT-2": "gpt2",
+    "ChatGPT": "EleutherAI/gpt-neo-1.3",
+    "LLaMa": "model_for_LLaMa",
+    "Vicuna": "model_for_Vicuna",
+    "Alpaca": "model_for_Alpaca",
+    "Flan-T5": "model_for_Flan-T5",
+}
 
-query_examples=[
-    ("Default Option 1"),
-    ("Default Option 2"),
-    ("Default Option 3")
-]
+# Define a function to generate text based on the selected model
+def generate_text(input_instruction, selected_model):
+    if selected_model in models:
+        model_name = models[selected_model]
+        pipe = pipeline("text-generation", model=model_name)
+        generated_text = pipe(input_instruction, max_length=100, do_sample=True)[0]['generated_text']
+        return generated_text
+    else:
+        return "Please select a model for this strategy."
 
-# Define a function that echoes the input text
-def echo_text(input_text):
-    return input_text
+# Define example instructions for testing
+instruction_examples = [
+    ("Write a short story about a cat."),
+    ("Explain the concept of artificial intelligence."),
+    ("Compose a poem about nature."),
+]
 
-with gr.Interface(
-    fn=echo_text,
-    inputs=gr.Textbox(placeholder="Enter text here..."),
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(placeholder="Enter instruction here..."),
     outputs=gr.Textbox(),
-    examples=query_examples, 
+    examples=instruction_examples,
     live=True,
-    title="LLM Evaluator with Linguistic Scrutiny",
-    theme=theme
-) as iface:
-    blocks = gr.Blocks()
-    
-    with gr.Row():
-        vicuna_model_selector = gr.Dropdown(["7b", "13b", "33b"], label="Vicuna Model", placeholder="Select model size")
-        llama_model_selector = gr.Dropdown(["7B", "13B", "30B", "65B"], label="LLaMa Model", placeholder="Select model size")
-        chatgpt_api_key = gr.Textbox(label="ChatGPT API Key", type="password", placeholder="Enter your API key")
-
-    # Strategy 1 - QA-Based Prompting
-    with gr.Accordion("Strategy 1 - QA-Based Prompting", style="font-weight: bold; font-size: 16px;"):
-        with gr.Row():
-            chatgpt_btn = gr.Button("ChatGPT")
-            llama_btn = gr.Button("LLaMA")
-            vicuna_btn = gr.Button("Vicuna")
-            alpaca_btn = gr.Button("Alpaca")
-            flant5_btn = gr.Button("Flan-T5")
-
-    # Strategy 2 - Instruction-Based Prompting
-    with gr.Accordion("Strategy 2 - Instruction-Based Prompting", style="font-weight: bold; font-size: 16px;"):
-        with gr.Row():
-            chatgpt_btn = gr.Button("ChatGPT")
-            llama_btn = gr.Button("LLaMA")
-            vicuna_btn = gr.Button("Vicuna")
-            alpaca_btn = gr.Button("Alpaca")
-            flant5_btn = gr.Button("Flan-T5")
-
-    # Strategy 3 - Structured Prompting
-    with gr.Accordion("Strategy 3 - Structured Prompting", style="font-weight: bold; font-size: 16px;"):
-        with gr.Row():
-            chatgpt_btn = gr.Button("ChatGPT")
-            llama_btn = gr.Button("LLaMA")
-            vicuna_btn = gr.Button("Vicuna")
-            alpaca_btn = gr.Button("Alpaca")
-            flant5_btn = gr.Button("Flan-T5")
-    
-    iface.launch()
+    title="Text Generation with Dynamic Model Selection",
+)
+
+# Additional input section 1 - User input
+with gr.Row():
+    user_input = gr.Textbox(placeholder="Enter your input...")
+
+# Additional input section 2 - Strategy 1
+with gr.Row():
+    strategy1_selector = gr.Dropdown(list(models.keys()), label="Strategy 1 - QA-Based Prompting")
+
+# Additional input section 3 - Strategy 2
+with gr.Row():
+    strategy2_selector = gr.Dropdown(list(models.keys()), label="Strategy 2 - Instruction-Based Prompting")
+
+# Additional input section 4 - Strategy 3
+with gr.Row():
+    strategy3_selector = gr.Dropdown(list(models.keys()), label="Strategy 3 - Structured Prompting")
+
+# Create a callback function for dynamic model selection
+def update_model_and_generate_text(input_instruction, selected_model):
+    if selected_model:
+        selected_model_name = strategy1_selector.value if selected_model == "Strategy 1 - QA-Based Prompting" else (
+            strategy2_selector.value if selected_model == "Strategy 2 - Instruction-Based Prompting" else
+            strategy3_selector.value)
+        iface.set_function(generate_text, inputs=[user_input, selected_model], outputs="outputs")
+        return "Selected model: " + selected_model_name
+
+# Add a submit button to trigger dynamic model selection
+submit_button = gr.Button("Submit")
+
+# Set the function for the Gradio interface to the update_model_and_generate_text function
+iface.fn = update_model_and_generate_text
+
+# Add the submit button to the interface
+iface.add(submit_button)
+
+iface.launch()

requirements.txt

@@ -3,4 +3,5 @@ torch
 openai
 sentencepiece
 fschat
-accelerate
+accelerate
+gpt2