Create app.py

app.py

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+# chatbot_app.py
+
+import streamlit as st
+import openai
+import requests
+from gtts import gTTS
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+from io import BytesIO
+from pydub import AudioSegment
+from pydub.playback import play
+import tempfile
+
+# Configure API keys
+HUGGING_FACE_API_KEY = "voicebot"
+OPENAI_API_KEY = "your_openai_api_key"
+openai.api_key = OPENAI_API_KEY
+
+# Initialize the Hugging Face model and tokenizer
+def load_model():
+    model_name = "facebook/musicgen-small"
+    tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=HUGGING_FACE_API_KEY)
+    model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=HUGGING_FACE_API_KEY)
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+# Function to convert voice to text using OpenAI's Whisper API
+def voice_to_text(audio_file):
+    with open(audio_file, "rb") as file:
+        transcript = openai.Audio.transcribe("whisper-1", file)
+    return transcript["text"]
+
+# Function to generate chatbot response using Hugging Face's model
+def generate_response(prompt):
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(inputs, max_length=100, num_return_sequences=1)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Function to convert text to voice using gTTS
+def text_to_speech(text):
+    tts = gTTS(text, lang="en")
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
+    tts.save(temp_file.name)
+    return temp_file.name
+
+# Streamlit app layout
+st.title("Voice-to-Text Chatbot")
+st.write("Speak to the chatbot and get responses in both text and audio!")
+
+# Upload audio file
+audio_file = st.file_uploader("Upload your voice input", type=["mp3", "wav", "ogg"])
+
+if audio_file is not None:
+    # Convert voice to text
+    with open("input_audio.wav", "wb") as f:
+        f.write(audio_file.read())
+    st.audio("input_audio.wav", format="audio/wav")
+
+    # Get text from audio
+    with st.spinner("Transcribing your voice..."):
+        user_input = voice_to_text("input_audio.wav")
+        st.write(f"**You said:** {user_input}")
+
+    # Generate chatbot response
+    with st.spinner("Generating response..."):
+        response_text = generate_response(user_input)
+        st.write(f"**Chatbot:** {response_text}")
+
+    # Convert response to audio
+    with st.spinner("Converting response to audio..."):
+        response_audio = text_to_speech(response_text)
+        audio_data = AudioSegment.from_mp3(response_audio)
+
+        # Display audio response
+        st.audio(response_audio, format="audio/mp3")
+
+        # Play audio
+        play(audio_data)
+
+    # Clean up temporary files
+    os.remove("input_audio.wav")
+    os.remove(response_audio)
+else:
+    st.write("Please upload an audio file to get started.")
+