Update app.py

app.py

@@ -1,72 +1,63 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from groq import Groq
 
-# Load the tokenizer and model directly
-tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-3B")
-model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-3B")
+# Initialize Groq client
+client = Groq(api_key='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')
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    # Compile the messages for context
-    messages = [{"role": "system", "content": system_message}]
-    
-    for val in history:
-        if val[0]:  # user message
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:  # assistant response
-            messages.append({"role": "assistant", "content": val[1]})
-    
-    messages.append({"role": "user", "content": message})
+# Function to generate responses with error handling
+def generate_response(user_input, chat_history):
+    try:
+        # Prepare messages with chat history
+        messages = [{"role": "system", "content": "You are a helpful mental health assistant."}]
+        for user_message, bot_response in chat_history:
+            messages.append({"role": "user", "content": user_message})
+            messages.append({"role": "assistant", "content": bot_response})
+        messages.append({"role": "user", "content": user_input})
 
-    # 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
-    )
+        # Call Groq API to get a response from LLaMA
+        chat_completion = client.chat.completions.create(
+            messages=messages,
+            model="llama3-8b-8192"
+        )
+        
+        # Extract response
+        response = chat_completion.choices[0].message.content
+        return response, chat_history
 
-    # 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
+    except Exception as e:
+        print(f"Error occurred: {e}")  # Print error to console for debugging
+        return "An error occurred while generating the response. Please try again.", chat_history
 
-# 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.
-"""
+# Define Gradio interface
+def gradio_interface():
+    with gr.Blocks() as demo:
+        # Variable to store chat history
+        chat_history = []
 
-# Set up the Gradio interface
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        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(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+        def generate_and_clear(user_input):
+            response, updated_history = generate_response(user_input, chat_history)
+            chat_history.append((user_input, response))
+            return response
+
+        def clear_chat():
+            chat_history.clear()
+            return "Chat history cleared. Start a new conversation!"
+
+        # Title and instructions
+        gr.Markdown("## Mental Health Chatbot - Powered by LLaMA on Groq")
+
+        # Chat interface setup
+        chatbot = gr.ChatInterface(
+            fn=generate_and_clear,
+            additional_inputs=[gr.Textbox(placeholder="Enter your message here...", label="Your Message")],
+            title="Mental Health Chatbot"
+        )
+
+        # Add button to clear chat history
+        clear_button = gr.Button("Clear Chat History")
+        clear_button.click(fn=clear_chat, inputs=[], outputs=chatbot)  # Clear chat on button click
 
-if __name__ == "__main__":
     demo.launch()
+
+# Run the interface
+gradio_interface()