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.env

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

.gitignore

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+.env
+.streamlit/secrets.toml
+secrets.toml

.streamlit/secrets.toml

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

README.md

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----
-title: Comminication Ai
-emoji: 👁
-colorFrom: purple
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.40.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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
+
+# Load environment variables
+load_dotenv()
+
+# Initialize Whisper Model
+@st.cache_resource
+def load_whisper_model():
+    return whisper.load_model("small")
+
+whisper_model = load_whisper_model()
+
+# Streamlit UI
+st.title("Conversational AI with Speech-to-Speech Response")
+st.write("Record your voice or upload an audio file to start the process.")
+
+# Sidebar Interaction Mode
+interaction_mode = st.sidebar.selectbox(
+    "Choose Interaction Mode:", ["Record Voice", "Upload Audio"]
+)
+
+# Record Voice Functionality with st.audio_input
+if interaction_mode == "Record Voice":
+    st.write("Use the audio recorder below to record your voice:")
+    
+    # Record audio using st.audio_input
+    audio_data = st.audio_input("Record your voice")
+    
+    if audio_data:
+        st.info("Recording received. Processing...")
+        
+        # Save the audio data to a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_audio:
+            temp_audio.write(audio_data.getvalue())  # Use .getvalue() to extract raw bytes
+            temp_audio_path = temp_audio.name
+
+        # Play back the saved audio
+        st.audio(temp_audio_path, format="audio/wav")
+        st.success("Audio saved and ready for transcription!")
+
+
+# Upload Audio Functionality
+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:
+        st.info("File uploaded. Saving...")
+        
+        # Save the uploaded audio file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            temp_audio.write(uploaded_file.read())  # Write uploaded audio content
+            temp_audio_path = temp_audio.name
+
+        # Play back the uploaded audio
+        st.audio(temp_audio_path, format="audio/mp3")
+        st.success("Audio uploaded and ready for transcription!")
+
+# Transcribe and Process Audio
+if 'temp_audio_path' in locals() and temp_audio_path:
+    st.write("Processing the audio file for transcription...")
+    
+    with st.spinner("Transcribing audio..."):
+        result = whisper_model.transcribe(temp_audio_path)
+        user_text = result["text"]
+        st.write("Transcribed Text:", user_text)
+        st.success("Transcription complete!")
+
+    # Generate AI Response
+    st.write("Generating a conversational response...")
+    
+    with st.spinner("Generating response..."):
+       
+        client = openai.OpenAI(
+            #Uncomment below if you want to use .env file for localhost or other deployment
+            #api_key=os.environ.get("SAMBANOVA_API_KEY"),
+
+            #for streamlit deployment
+            api_key= st.secrets["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)
+        st.success("Response generated!")
+
+    # Convert response text to speech using gTTS
+    st.write("Converting the response to speech...")
+    
+    with st.spinner("Converting text to speech..."):
+        tts = gTTS(text=answer, slow=False)
+        response_audio_path = "final_response.mp3"
+        tts.save(response_audio_path)
+        st.success("Conversion complete!")
+
+    # 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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+libportaudio2
+python3-all-dev
+ffmpeg

requirements.txt

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+openai-whisper
+gTTS
+python-dotenv
+openai
+streamlit
+sounddevice
+numpy
+torch
+ffmpeg