Update app.py

app.py

@@ -1,7 +1,5 @@
-import gradio as gr
-import speech_recognition as sr
+mport gradio as gr
 from huggingface_hub import InferenceClient
-from io import BytesIO
 
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
@@ -13,44 +11,29 @@ def respond(
     temperature,
     top_p,
 ):
-    try:
-        messages = [{"role": "system", "content": system_message}]
+    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]})
+    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})
+    messages.append({"role": "user", "content": message})
 
-        response = ""
+    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
-    
-    except Exception as e:
-        yield f"An error occurred: {str(e)}"
+    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
 
-def recognize_speech(audio_file):
-    recognizer = sr.Recognizer()
-    with sr.AudioFile(audio_file) as source:
-        audio_data = recognizer.record(source)
-        try:
-            text = recognizer.recognize_google(audio_data)
-            return text
-        except sr.UnknownValueError:
-            return "Sorry, I could not understand the audio."
-        except sr.RequestError as e:
-            return f"Could not request results; {e}"
+        response += token
+        yield response
 
 # Define custom CSS
 custom_css = """
@@ -87,23 +70,22 @@ body {
 """
 
 # Create a Gradio chat interface with custom CSS
-with gr.Blocks(css=custom_css) as demo:
-    system_msg = gr.Textbox(value="You are a Chatbot. Your name is Evy. You are Developed By Joe.", label="System message")
-    max_tokens = gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens")
-    temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
-    top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
-    
-    input_textbox = gr.Textbox(label="Your message")
-    voice_input = gr.Audio(source="microphone", type="file", label="Record your message")
-    with gr.Row():
-        submit_button = gr.Button("Send")
-        voice_button = gr.Button("🎤 Speak")
-        
-    chatbot_output = gr.Textbox(label="Chatbot response", interactive=False)
+demo = gr.ChatInterface(
+    fn=respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a Chatbot.Your name is Evy.Your are Developed By Joe.", 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)",
+        ),
+    ],
+    css=custom_css
+)
 
-    submit_button.click(respond, inputs=[input_textbox, gr.State([]), system_msg, max_tokens, temperature, top_p], outputs=chatbot_output)
-    voice_input.change(lambda x: recognize_speech(BytesIO(x)), inputs=[voice_input], outputs=input_textbox)
-    
-# Launch the interface
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()