Update app.py

app.py

@@ -1,61 +1,61 @@
-import os
-import gradio as gr
-import whisper
-from groq import Groq
-from gtts import gTTS
-import tempfile
-
-# Initialize Whisper model
-model = whisper.load_model("base")
-
-# Initialize Groq client
-client = Groq(
-    api_key="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",
-)
-
-# Function for speech-to-text using Whisper
-def transcribe_audio(audio):
-    # Transcribe the audio using Whisper
-    result = model.transcribe(audio)
-    return result["text"]
-
-# Function for generating response using Llama 8B
-def generate_response(transcription):
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {
-                "role": "user",
-                "content": transcription,
-            }
-        ],
-        model="llama3-8b-8192",
-    )
-    return chat_completion.choices[0].message.content
-
-# Function for text-to-speech using gTTS
-def text_to_speech(response):
-    tts = gTTS(response)
-    temp_audio = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
-    tts.save(temp_audio.name)
-    return temp_audio.name
-
-# Function that integrates the full pipeline
-def chatbot(audio):
-    transcription = transcribe_audio(audio)
-    response = generate_response(transcription)
-    audio_output = text_to_speech(response)
-    return transcription, response, audio_output
-
-# Gradio Interface
-iface = gr.Interface(
-    fn=chatbot, 
-    inputs=gr.Audio(type="filepath"),  # Removed the 'source' argument
-    outputs=[
-        gr.Textbox(label="Transcription"), 
-        gr.Textbox(label="Llama Response"), 
-        gr.Audio(label="Response Audio")
-    ],
-    live=True
-)
-
-iface.launch()
+import os
+import gradio as gr
+import whisper
+from groq import Groq
+from gtts import gTTS
+import tempfile
+
+# Initialize Whisper model
+model = whisper.load_model("base")
+
+# Initialize Groq client
+client = Groq(
+    api_key = os.environ.get("GROQ_API_KEY"),
+)
+
+# Function for speech-to-text using Whisper
+def transcribe_audio(audio):
+    # Transcribe the audio using Whisper
+    result = model.transcribe(audio)
+    return result["text"]
+
+# Function for generating response using Llama 8B
+def generate_response(transcription):
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": transcription,
+            }
+        ],
+        model="llama3-8b-8192",
+    )
+    return chat_completion.choices[0].message.content
+
+# Function for text-to-speech using gTTS
+def text_to_speech(response):
+    tts = gTTS(response)
+    temp_audio = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
+    tts.save(temp_audio.name)
+    return temp_audio.name
+
+# Function that integrates the full pipeline
+def chatbot(audio):
+    transcription = transcribe_audio(audio)
+    response = generate_response(transcription)
+    audio_output = text_to_speech(response)
+    return transcription, response, audio_output
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=chatbot, 
+    inputs=gr.Audio(type="filepath"),  # Removed the 'source' argument
+    outputs=[
+        gr.Textbox(label="Transcription"), 
+        gr.Textbox(label="Llama Response"), 
+        gr.Audio(label="Response Audio")
+    ],
+    live=True
+)
+
+iface.launch()