Update app.py

app.py

@@ -1,64 +1,50 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
+# This is a Gradio app that simulates the functionality of the provided Tkinter application.
+# It includes a text input, a voice input button, and a response area to display the AI's response.
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import asyncio
+from ai_system.ai_core import AICore
+import speech_recognition as sr
+
+# Initialize the AI core
+ai = AICore()
+
+# Function to process the query and get the AI response
+async def process_query(query):
+    result = await ai.generate_response(query, 1)
+    return result['response']
+
+# Function to handle the text input submission
+def submit_query(query):
+    if not query:
+        return ""
+    response = asyncio.run(process_query(query))
+    return f"Response: {response}"
+
+# Function to handle voice input
+def listen_voice_command():
+    recognizer = sr.Recognizer()
+    with sr.Microphone() as source:
+        print("Listening for voice command...")
+        audio = recognizer.listen(source)
+        try:
+            command = recognizer.recognize_google(audio)
+            return command
+        except sr.UnknownValueError:
+            return "Voice command not recognized."
+        except sr.RequestError:
+            return "Could not request results; check your network connection."
+
+# Create the Gradio interface
+with gr.Blocks() as demo:
+    query_entry = gr.Textbox(label="Enter your query")
+    response_area = gr.Textbox(label="Response")
+    submit_button = gr.Button("Submit")
+    voice_button = gr.Button("Voice Input")
+
+    # Set up the event listeners
+    submit_button.click(submit_query, inputs=query_entry, outputs=response_area)
+    voice_button.click(listen_voice_command, outputs=query_entry)
+
+# Launch the Gradio app
+demo.launch(show_error=True)