Update app.py

app.py

@@ -1,13 +1,20 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Initialize the Zephyr-7B client
+zephyr_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load your fine-tuned GPT-2 model from Hugging Face
+MODEL_NAME = "hackergeek98/therapist01"  # Replace with your model name
+gpt2_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+gpt2_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
 
+# Initialize conversation history for GPT-2
+conversation_history = ""
 
-def respond(
+# Function to generate responses using Zephyr-7B
+def respond_with_zephyr(
     message,
     history: list[tuple[str, str]],
     system_message,
@@ -27,7 +34,7 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    for message in zephyr_client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
@@ -39,51 +46,21 @@ def respond(
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-# Load your fine-tuned GPT-2 model from Hugging Face
-MODEL_NAME = "hackergeek98/therapist01"  # Replace w 
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
-
-# Initialize conversation history
-conversation_history = ""
-
-# Function to generate responses
-def generate_response(user_input):
+# Function to generate responses using GPT-2
+def respond_with_gpt2(user_input):
     global conversation_history
 
     # Update conversation history with user input
     conversation_history += f"User: {user_input}\n"
     
     # Tokenize the conversation history
-    inputs = tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024)
+    inputs = gpt2_tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024)
 
     # Generate a response from the model
-    outputs = model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2)
+    outputs = gpt2_model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2)
 
     # Decode the model's output
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = gpt2_tokenizer.decode(outputs[0], skip_special_tokens=True)
 
     # Update conversation history with the model's response
     conversation_history += f"Therapist: {response}\n"
@@ -91,16 +68,29 @@ def generate_response(user_input):
     # Return the therapist's response
     return response
 
+# Function to handle the model selection and response generation
+def respond(message, history, model_choice, system_message, max_tokens, temperature, top_p):
+    if model_choice == "Zephyr-7B":
+        return respond_with_zephyr(message, history, system_message, max_tokens, temperature, top_p)
+    elif model_choice == "GPT-2 Therapist":
+        return respond_with_gpt2(message)
+    else:
+        return "Invalid model selection."
+
 # Create Gradio interface
-interface = gr.Interface(fn=generate_response,
-                         inputs=gr.Textbox(label="Enter your message", lines=2),
-                         outputs=gr.Textbox(label="Therapist Response", lines=2),
-                         title="Virtual Therapist",
-                         description="A fine-tuned GPT-2 model acting as a virtual therapist. Chat with the model and receive responses as if you are talking to a therapist.")
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Dropdown(choices=["Zephyr-7B", "GPT-2 Therapist"], label="Model", value="Zephyr-7B"),
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ],
+    title="Multi-Model Chat Interface",
+    description="Choose between Zephyr-7B and a fine-tuned GPT-2 model to chat with."
+)
 
 # Launch the app
-interface.launch()
-
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()