Update app.py

app.py

@@ -1,54 +1,49 @@
 import os
-import whisper
-from gtts import gTTS
+from dotenv import load_dotenv
 from groq import Groq
-import gradio as gr
-from tempfile import NamedTemporaryFile
+import streamlit as st
 
-# Set up Groq client
-client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+# Load environment variables
+load_dotenv()
 
-# Load OpenAI Whisper model
-whisper_model = whisper.load_model("base")
-
-def voice_to_voice_chat(audio_file):
-    # Step 1: Transcribe Audio Input
-    transcription = whisper_model.transcribe(audio_file)["text"]
-
-    # Step 2: Query Groq's LLM
-    chat_completion = client.chat.completions.create(
-        messages=[
-            {"role": "user", "content": transcription}
-        ],
-        model="llama3-8b-8192",
-        stream=False,
-    )
-    llm_response = chat_completion.choices[0].message.content
-
-    # Step 3: Convert LLM Response to Audio
-    tts = gTTS(llm_response)
-    audio_output = NamedTemporaryFile(delete=False, suffix=".mp3")
-    tts.save(audio_output.name)
-
-    return llm_response, audio_output.name
-
-# Gradio Interface
-def chatbot_interface(audio_input):
-    response, audio_response_file = voice_to_voice_chat(audio_input)
-    return response, audio_response_file
-
-# Build Gradio App
-interface = gr.Interface(
-    fn=chatbot_interface,
-    inputs=gr.Audio(source="microphone", type="filepath", label="Speak into the Microphone"),
-    outputs=[
-        gr.Textbox(label="Chatbot Response"),
-        gr.Audio(type="filepath", label="Voice Output")
-    ],
-    title="Real-Time Voice-to-Voice Chatbot",
-    description="Speak to the chatbot and get a spoken response!",
+# Initialize Groq client
+client = Groq(
+    api_key=os.environ.get("GROQ_API_KEY"),
 )
 
-# Launch Interface
-if __name__ == "__main__":
-    interface.launch()
+# Streamlit UI
+st.title("Python Bot with Groq's API")
+
+# User input
+user_input = st.text_input("Enter your question or message:")
+
+# Submit button
+if st.button("Submit"):
+    if user_input.strip():
+        # Interact with Groq's LLM
+        with st.spinner("Fetching response from the LLM..."):
+            try:
+                chat_completion = client.chat.completions.create(
+                    messages=[
+                        {"role": "user", "content": user_input},
+                    ],
+                    model="llama3-8b-8192",
+                    stream=False,
+                )
+                response = chat_completion.choices[0].message.content
+                st.success("Response received:")
+                st.write(response)
+            except Exception as e:
+                st.error(f"Error: {e}")
+    else:
+        st.warning("Please enter a valid message!")
+
+# Deployment instructions
+st.markdown(
+    """
+    ### Deployment
+    - Install `streamlit` and `groq` locally.
+    - Run this app with `streamlit run your_script_name.py`.
+    - Push it to Hugging Face for wider accessibility.
+    """
+)