Update app.py

app.py

@@ -1,8 +1,9 @@
 import gradio as gr
-from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# Use a pipeline as a high-level helper for text generation
-pipe = pipeline("text2text-generation", model="facebook/blenderbot-3B")
+# Load the tokenizer and model directly
+tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-3B")
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-3B")
 
 def respond(
     message,
@@ -12,34 +13,49 @@ def respond(
     temperature,
     top_p,
 ):
+    # Compile the messages for context
     messages = [{"role": "system", "content": system_message}]
-
+    
     for val in history:
-        if val[0]:
+        if val[0]:  # user message
             messages.append({"role": "user", "content": val[0]})
-        if val[1]:
+        if val[1]:  # assistant response
             messages.append({"role": "assistant", "content": val[1]})
-
-    # Add the user's current message to the conversation context
+    
     messages.append({"role": "user", "content": message})
 
-    # The pipeline expects a list of texts, so we will generate a response
+    # Concatenate messages as input text
     input_text = "\n".join([msg["content"] for msg in messages if msg["role"] == "user"])
+    
+    # Tokenize input text and generate response
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(
+        **inputs,
+        max_length=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+        do_sample=True
+    )
 
-    # Generate the response
-    response = pipe(input_text, max_length=max_tokens, temperature=temperature, top_p=top_p)
-
-    # Return the generated response
-    yield response[0]['generated_text']
-
+    # Decode the generated response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Return response iteratively as tokens arrive (optional: can remove yield if streaming is not needed)
+    yield response
 
+# Customize the system message for mental health support
+default_system_message = """
+You are a compassionate mental health specialist trained to listen empathetically and offer support. 
+When engaging with users, make sure to respond with kindness and provide general emotional support. 
+Avoid giving specific medical or clinical advice, but offer guidance, validate feelings, and suggest appropriate resources when needed. 
+Encourage open conversations and create a safe, non-judgmental space for the user to share.
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+
+# Set up the Gradio interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value=default_system_message, label="System Message (Mental Health Specialist)"),
         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(
@@ -52,6 +68,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()