Create app.py

app.py

@@ -0,0 +1,91 @@
+import gradio as gr
+from groq import Groq
+
+# Groq API Configuration
+client = Groq(api_key = "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")
+
+# Function to handle responses from Groq
+def chatbot(message, history, system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}]
+    
+    # Add conversation history
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+    
+    # Append current user message
+    messages.append({"role": "user", "content": message})
+    
+    # Call the Groq API to generate the response
+    response = ""
+    for chunk in client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=messages,
+        temperature=temperature,
+        max_tokens=max_tokens,
+        top_p=top_p,
+        stream=True,
+    ):
+        response += chunk.choices[0].delta.content or ""
+        yield response
+
+# System message to inform the AI about its purpose
+system_message = "You are Jude Mental Health AI Chat. You are an experienced expert in mental health, psychology, and psychiatry. You have a wide range of knowledge, skills, and tools for dealing with mental health issues, and you are also a good educator on mental health issues. Provide concise, informed answers to various prompts on mental health"
+
+# Define the Gradio interface
+def create_interface():
+    with gr.Blocks() as demo:
+        # Display the main header
+        gr.Markdown("# Welcome to Jude Mental Health AI Chat\n # What can we help you with?")
+        
+        # Chatbot output area (AI's response)
+        chatbot_output = gr.Chatbot(label="Chat with Jude Mental Health AI")
+        
+        # Textbox for user input (prompt)
+        with gr.Row():
+            user_input = gr.Textbox(placeholder="Enter your question here...", label="Ask a question", interactive=True, elem_id="user_input", lines=3)
+            submit_button = gr.Button("Send", elem_id="submit_button")  # Shorter button
+        
+        # Mental Health Topics Buttons
+        with gr.Row():
+            topic_button_anxiety = gr.Button("Anxiety")
+            topic_button_depression = gr.Button("Depression")
+            topic_button_sleep = gr.Button("Impact of Sleep")
+            topic_button_drugs = gr.Button("Drugs & Mental Health")
+            topic_button_more = gr.Button("More")
+        
+        # Gradio sliders to control the parameters for response generation
+        max_tokens_slider = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max Tokens")
+        temperature_slider = gr.Slider(minimum=0.1, maximum=2.0, value=0.7, step=0.1, label="Temperature")
+        top_p_slider = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+
+        # Action when submit button is clicked
+        def on_submit(message, history, max_tokens, temperature, top_p):
+            history.append((message, ""))
+            for response in chatbot(message, history, system_message, max_tokens, temperature, top_p):
+                history[-1] = (message, response)  # Update the history with the new response
+            return history, gr.update(value="")  # Clear the input textbox after submission
+        
+        submit_button.click(on_submit, inputs=[user_input, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
+
+        # Action when topic button is clicked
+        def on_topic_click(topic, history, max_tokens, temperature, top_p):
+            topic_message = f"Tell me about {topic}."
+            history.append((topic_message, ""))
+            for response in chatbot(topic_message, history, system_message, max_tokens, temperature, top_p):
+                history[-1] = (topic_message, response)
+            return history, gr.update(value="")  # Clear the input textbox after topic selection
+        
+        topic_button_anxiety.click(on_topic_click, inputs=[topic_button_anxiety, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
+        topic_button_depression.click(on_topic_click, inputs=[topic_button_depression, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
+        topic_button_sleep.click(on_topic_click, inputs=[topic_button_sleep, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
+        topic_button_drugs.click(on_topic_click, inputs=[topic_button_drugs, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
+        topic_button_more.click(on_topic_click, inputs=[topic_button_more, gr.State([]), max_tokens_slider, temperature_slider, top_p_slider], outputs=[chatbot_output, user_input])
+
+    return demo
+
+# Launch the interface
+demo = create_interface()
+demo.launch()