Update app.py

app.py

@@ -1,79 +1,34 @@
-from transformers import pipeline
-generator = pipeline('text-generation', model = 'isitcoding/gpt2_120_finetuned')
-generator("", max_length = 1028, num_return_sequences=3)
-
-'''import os
 import gradio as gr
+import random
+import time
 from transformers import pipeline
-from huggingface_hub import InferenceClient
-
-hf_token = os.getenv("gpt2_token")
-# Initialize the text generation pipeline
-client = 
-generator = pipeline("text-generation", )
-
-# Define the response function with additional options for customization
-def text_generation(
-    prompt: str,
-    details: bool = False,
-    stream: bool = False,
-    model: str = None,
-    best_of: int = None,
-    decoder_input_details: bool = None,
-    do_sample: bool = False,
-    frequency_penalty: float = None,
-    grammar: None = None,
-    max_new_tokens: int = None,
-    repetition_penalty: float = None
-):
-    # Setup the configuration for the model generation
-    gen_params = {
-        "max_length": 518,  # Default, you can tweak it or set from parameters
-        "num_return_sequences": 1,
-        "do_sample": do_sample,
-        "temperature": 0.7,  # Controls randomness
-        "top_k": 50,  # You can adjust for more control over sampling
-        "top_p": 0.9,  # Same as above, for sampling
-    }
-
-    if max_new_tokens:
-        gen_params["max_length"] = max_new_tokens + len(prompt.split())
-
-    if frequency_penalty:
-        gen_params["frequency_penalty"] = frequency_penalty
-
-    if repetition_penalty:
-        gen_params["repetition_penalty"] = repetition_penalty
-
-    # Generate the text based on the input prompt and parameters
-    generated_text = generator(prompt, **gen_params)[0]["generated_text"]
-
-    if details:
-        # Return additional details for debugging if needed
-        return {
-            "generated_text": generated_text,
-            "params_used": gen_params
-        }
-    else:
-        return generated_text
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=text_generation,  # The function we defined
-    inputs=[
-        gr.Textbox(label="Input Prompt"),  # User input prompt
-        gr.Checkbox(label="Show Details", default=False),  # Option for additional details
-        gr.Checkbox(label="Stream Mode", default=False),  # Streaming checkbox (not used in this example)
-        gr.Textbox(label="Model (optional)", default=None),  # Optional model name
-        gr.Slider(minimum=1, maximum=5, label="Best of (Optional)", default=None),
-        gr.Slider(minimum=0.0, maximum=2.0, label="Frequency Penalty (Optional)", default=None),
-        gr.Slider(minimum=0.0, maximum=2.0, label="Repetition Penalty (Optional)", default=None),
-    ],
-    outputs="text"  # Output is plain text
-)
-
-# Launch the interface
-iface.launch()
-'''
 
 
+# Load the text generation pipeline with your fine-tuned model
+generator = pipeline('text-generation', model='isitcoding/gpt2_120_finetuned')
+
+# Function to generate responses using the text generation model
+def respond(message, chat_history):
+    # Generate a response from the model
+    response = generator(message, max_length=1028, num_return_sequences=3)[0]['generated_text']
+    # Append the user message and model response to chat history
+    chat_history.append(("User", message))
+    chat_history.append(("Bot", response))
+    return chat_history
+
+# Create a Gradio interface using Blocks
+with gr.Blocks() as demo:
+    # Add a Chatbot component
+    chatbot = gr.Chatbot()
+    # Add a textbox for user input
+    msg = gr.Textbox(label="Enter your message")
+    # Add a button to clear the chat
+    clear = gr.Button("Clear")
+
+    # Define what happens when the user submits a message
+    msg.submit(respond, [msg, chatbot], chatbot)
+    # Define what happens when the clear button is pressed
+    clear.click(lambda: [], None, chatbot)
+
+# Launch the Gradio interface
+demo.launch()