Update app.py

app.py

@@ -1,48 +1,29 @@
-from huggingface_hub import InferenceClient
+#from huggingface_hub import InferenceClient
 import gradio as gr
+from transformers import pipeline
 
-client = InferenceClient("grammarly/coedit-large")
+# Load the model and tokenizer using the pipeline API
+model_pipeline = pipeline("text-generation", model="grammarly/coedit-large")
 
-def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, top_k=50, repetition_penalty=1.0):
-    print(f"  TEMP: {temperature}     \n\t TYPE: {type(temperature)}")
-    print(f"  TOP-P: {top_p}     \n\t TYPE: {type(top_p)}")
-    print(f"  TOP-K: {top_k}     \n\t TYPE: {type(top_k)}")
-    print(f"  MAX_TOK: {max_new_tokens}     \n\t TYPE: {type(max_new_tokens)}")
-    
-    
-    #temperature = float(temperature)
-    temperature = float(temperature[0]) if isinstance(temperature, list) else float(temperature)
-    if temperature < 1e-2: temperature = 1e-2
-    top_p = float(top_p)
-    top_k = int(top_k)  # Ensure top_k is an integer, as it was being treated like a float
-
-    generate_kwargs = dict(temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, top_k=top_k, repetition_penalty=repetition_penalty)  # seed=42,)
+def generate_text(input_text, temperature=0.9, max_new_tokens=50, top_p=0.95, top_k=50):
+    # Generate text using the model
+    output = model_pipeline(input_text, temperature=temperature, max_length=max_new_tokens + len(input_text.split()), top_p=top_p, top_k=top_k, return_full_text=False)
+    # Extract and return the generated text
+    return output[0]['generated_text']
 
-    formatted_prompt = "Fix grammatical errors in this sentence: " + prompt
-    print("\nPROMPT: \n\t" + formatted_prompt)
-
-    # Generate text from the HF inference
-    output = client.text_generation(formatted_prompt, **generate_kwargs, details=True, return_full_text=True)
-    #output = ""
-
-    #for response in stream:
-    #    output += response.token.text
-    #    yield output
-    return output
     
 
+# Define your Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.inputs.Textbox(lines=2, label="Input Text"),
+        gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.9, label="Temperature"),
+        gr.inputs.Slider(minimum=1, maximum=100, step=1, default=50, label="Max New Tokens"),
+        gr.inputs.Slider(minimum=0, maximum=1, step=0.01, default=0.95, label="Top-p"),
+        gr.inputs.Slider(minimum=0, maximum=100, step=1, default=50, label="Top-k")
+    ],
+    outputs=[gr.outputs.Textbox(label="Generated Text")],
+    title="Text Generation with Grammarly Model"
+)
 
-additional_inputs=[
-    gr.Slider( label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs", ),
-    gr.Slider( label="Max new tokens", value=150, minimum=0, maximum=250, step=64, interactive=True, info="The maximum numbers of new tokens", ),
-    gr.Slider( label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens", ),
-    gr.Slider( label="Top-k", value=50, minimum=0, maximum=100, step=1, interactive=True, info="Limits the number of top-k tokens considered at each step"),
-]
-
-gr.ChatInterface(
-    fn=generate,
-    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
-    additional_inputs=additional_inputs,
-    title="My Grammarly Space",
-    concurrency_limit=20,
-).launch(show_api=False)