Update app.py

app.py

@@ -1,17 +1,32 @@
-from gradio_client import Client
+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
-# Use the space URL where your Gradio app is hosted
-client = Client("BruceLee1234/HelpingAIMentalAssistance")
+# Load the GPT-2 model and tokenizer
+model_name = "gpt2"  # You can also try "gpt2-medium" or "gpt2-large" for more powerful models
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
 
-result = client.predict(
-    message="Hello, Hope you are having a great day!!",
-    system_message="You are HelpingAI mental assistance. You identify the flow of dialogue the user chats and respond in the same emotional state. Make it realistic in HelpfulAI style",
-    max_tokens=2048,
-    temperature=0.7,
-    top_p=0.95,
-    top_k=40,
-    repeat_penalty=1.1,
-    api_name="/chat"  # This is the endpoint exposed by Hugging Face
+# Define the chat function
+def chat_function(user_input):
+    # Encode the input
+    inputs = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
+    
+    # Generate a response from the model
+    outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)
+    
+    # Decode the generated response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=chat_function,          # Function to call
+    inputs=gr.Textbox(label="You"),  # Textbox for user input
+    outputs=gr.Textbox(label="Chatbot Response"),  # Textbox for model output
+    title="GPT-2 Chatbot",         # Title of the app
+    description="A simple chatbot powered by GPT-2. Enter a message to chat with the bot."  # Description
 )
 
-print(result)
+# Launch the app
+iface.launch()