Create app.py

app.py

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+
+
+import gradio as gr
+import uuid
+import os
+import speech_recognition as sr
+from gtts import gTTS
+from langchain_community.llms import Ollama
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.runnables.history import RunnableWithMessageHistory
+
+# Initialize the model and prompt template
+chat = Ollama(model="llama3:latest")
+
+prompt = ChatPromptTemplate.from_messages([
+    ("system", """
+    You are a helpful AI assistant. Your task is to engage in conversation with users,
+    answer their questions, and assist them with various tasks.
+    Communicate politely and maintain focus on the user's needs.
+    Keep responses concise, typically two to three sentences.
+    """),
+    MessagesPlaceholder(variable_name="history"),
+    ("human", "{input}"),
+])
+
+runnable = prompt | chat
+
+with_message_history = RunnableWithMessageHistory(
+    runnable,
+    lambda session_id: ChatMessageHistory(),
+    input_messages_key="input",
+    history_messages_key="history",
+)
+
+def text_to_speech(text, file_name):
+    tts = gTTS(text=text, lang='en', slow=False)
+    file_path = os.path.join(os.getcwd(), file_name)
+    tts.save(file_path)
+    return file_path
+
+def speech_to_text(audio):
+    if audio is None:
+        return "No audio input received."
+    
+    recognizer = sr.Recognizer()
+    try:
+        with sr.AudioFile(audio) as source:
+            audio_data = recognizer.record(source)
+        try:
+            text = recognizer.recognize_google(audio_data)
+            print(text)
+            return text
+        except sr.UnknownValueError:
+            return "Speech recognition could not understand the audio"
+        except sr.RequestError:
+            return "Could not request results from the speech recognition service"
+    except Exception as e:
+        return f"Error processing audio: {str(e)}"
+
+def chat_function(input_type, text_input=None, audio_input=None, history=None):
+    if history is None:
+        history = []
+    
+    if input_type == "text":
+        user_input = text_input
+    elif input_type == "audio":
+        if audio_input is not None:
+            user_input = speech_to_text(audio_input)
+        else:
+            user_input = "No audio input received."
+    else:
+        return history, history, None
+
+    print(f"User input: {user_input}")  # Debug information
+
+    # Get LLM response
+    response = with_message_history.invoke(
+        {"input": user_input},
+        config={"configurable": {"session_id": "chat_history"}},
+    )
+
+    # Generate audio for LLM response
+    audio_file = f"response_{uuid.uuid4()}.mp3"
+    audio_path = text_to_speech(response, audio_file)
+
+    # Update history in the correct format
+    history.append((user_input, response))
+    
+    return history, history, audio_path
+
+# Gradio interface
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot()
+    with gr.Row():
+        text_input = gr.Textbox(placeholder="Type your message here...")
+        audio_input = gr.Audio(sources=['microphone'], type="filepath")
+    with gr.Row():
+        text_button = gr.Button("Send Text")
+        audio_button = gr.Button("Send Audio")
+    audio_output = gr.Audio()
+
+    def on_audio_change(audio):
+        if audio is not None:
+            return speech_to_text(audio)
+        return ""
+
+    audio_input.change(on_audio_change, inputs=[audio_input], outputs=[text_input])
+    text_button.click(chat_function, inputs=[gr.Textbox(value="text"), text_input, audio_input, chatbot], outputs=[chatbot, chatbot, audio_output])
+    audio_button.click(chat_function, inputs=[gr.Textbox(value="audio"), text_input, audio_input, chatbot], outputs=[chatbot, chatbot, audio_output])
+
+demo.launch(server_name='192.168.3.151',share=True,upload_limit=10,  max_threads=10)