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.env

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+SAMBANOVA_API_KEY=6f77154e-13ca-4e74-869d-183684dc7b3f

.gitignore

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+.env

app.py

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+import os
+import whisper
+from gtts import gTTS
+from dotenv import load_dotenv
+import openai
+import streamlit as st
+import tempfile
+from pydub import AudioSegment
+import wave
+import pyaudio
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Whisper Model
+@st.cache_resource
+def load_whisper_model():
+    return whisper.load_model("medium")
+
+whisper_model = load_whisper_model()
+
+# Streamlit UI
+st.title("Conversational AI with Speech-to-Speech Response")
+st.write("Upload an audio file or record your voice to start the process.")
+
+# Add a sidebar for interaction options
+interaction_mode = st.sidebar.selectbox(
+    "Choose Interaction Mode:", ["Record Voice", "Upload Audio"]
+)
+
+# Record Voice Functionality using pydub and pyaudio
+def record_audio(filename, duration=5, sample_rate=44100):
+    st.info(f"Recording for {duration} seconds...")
+    p = pyaudio.PyAudio()
+    
+    # Open a stream for recording
+    stream = p.open(format=pyaudio.paInt16, channels=1, rate=sample_rate, input=True, frames_per_buffer=1024)
+    
+    frames = []
+    for _ in range(0, int(sample_rate / 1024 * duration)):
+        data = stream.read(1024)
+        frames.append(data)
+    
+    stream.stop_stream()
+    stream.close()
+    p.terminate()
+    
+    # Save the recorded frames as a WAV file
+    with wave.open(filename, 'wb') as wf:
+        wf.setnchannels(1)
+        wf.setsampwidth(p.get_sample_size(pyaudio.paInt16))
+        wf.setframerate(sample_rate)
+        wf.writeframes(b''.join(frames))
+
+    st.success("Recording complete!")
+
+# Process Audio Input
+if interaction_mode == "Record Voice":
+    duration = st.slider("Select Recording Duration (seconds):", min_value=10, max_value=120, step=10)
+    record_btn = st.button("Start Recording")
+    
+    if record_btn:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
+            record_audio(temp_audio.name, duration=duration)
+            temp_audio_path = temp_audio.name
+            st.audio(temp_audio_path, format="audio/wav")
+elif interaction_mode == "Upload Audio":
+    uploaded_file = st.file_uploader("Upload your audio file (MP3/WAV)", type=["mp3", "wav"])
+    if uploaded_file is not None:
+        # Save the uploaded file temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            temp_audio.write(uploaded_file.read())
+            temp_audio_path = temp_audio.name
+            st.audio(temp_audio_path, format="audio/mp3")
+
+# Process and Transcribe Audio
+if 'temp_audio_path' in locals() and temp_audio_path is not None:
+    st.write("Processing the audio file...")
+    
+    # If the uploaded or recorded audio is in MP3 format, convert it to WAV for Whisper
+    if temp_audio_path.endswith(".mp3"):
+        audio = AudioSegment.from_mp3(temp_audio_path)
+        temp_audio_path = temp_audio_path.replace(".mp3", ".wav")
+        audio.export(temp_audio_path, format="wav")
+
+    # Transcribe audio using Whisper
+    result = whisper_model.transcribe(temp_audio_path)
+    user_text = result["text"]
+    st.write("Transcribed Text:", user_text)
+
+    # Generate AI Response
+    st.write("Generating a conversational response...")
+    client = openai.OpenAI(
+        api_key=os.environ.get("SAMBANOVA_API_KEY"),
+        base_url="https://api.sambanova.ai/v1",
+    )
+    
+    response = client.chat.completions.create(
+        model='Meta-Llama-3.1-8B-Instruct',
+        messages=[
+            {"role": "system", "content": (
+                "You are a kind, empathetic, and intelligent assistant capable of meaningful conversations and emotional support. "
+                "Your primary goals are: "
+                "1. To engage in casual, friendly, and supportive conversations when the user seeks companionship or emotional relief. "
+                "2. To adapt your tone and responses to match the user's mood, providing warmth and encouragement if they seem distressed or seeking emotional support. "
+                "3. To answer questions accurately and provide explanations when asked, adjusting the depth and length of your answers based on the user's needs. "
+                "4. To maintain a positive and non-judgmental tone, offering helpful advice or lighthearted dialogue when appropriate. "
+                "5. To ensure the user feels heard, understood, and valued during every interaction. "
+                "If the user does not ask a question, keep the conversation engaging and meaningful by responding thoughtfully or with light humor where appropriate."
+            )},
+            {"role": "user", "content": user_text},
+        ],
+        temperature=0.1,
+        top_p=0.1,
+    )
+    
+    answer = response.choices[0].message.content
+    st.write("Response:", answer)
+
+    # Convert response text to speech using gTTS
+    st.write("Converting the response to speech...")
+    tts = gTTS(text=answer,  slow=False)
+    response_audio_path = "final_response.mp3"
+    tts.save(response_audio_path)
+
+    # Play and download the response MP3
+    st.audio(response_audio_path, format="audio/mp3")
+    st.download_button(
+        label="Download the Response",
+        data=open(response_audio_path, "rb"),
+        file_name="final_response.mp3",
+        mime="audio/mpeg",
+    )
+
+    # Clean up temporary files
+    os.remove(temp_audio_path)
+    os.remove(response_audio_path)

packages.txt

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+portaudio19-dev
+python3-all-dev
+ffmpeg

requirements.txt

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+openai-whisper
+gTTS
+python-dotenv
+openai
+streamlit
+pydub
+pyaudio
+numpy
+torch
+ffmpeg