Create app.py

app.py

@@ -0,0 +1,84 @@
+import os
+from groq import Groq
+import streamlit as st
+from dotenv import load_dotenv
+
+# Load API key from .env file
+load_dotenv()
+api_key = os.getenv("GROQ_API_KEY")
+
+# Initialize the Groq client
+client = Groq(api_key=api_key)
+
+# Define the programming development topics for the chatbot
+developer_topics = [
+    "best programming languages", "web development frameworks", "version control with Git", 
+    "debugging tips", "data structures and algorithms", "object-oriented programming", 
+    "functional programming", "software design patterns", "API design and development", 
+    "devops practices", "cloud computing", "front-end development", "back-end development", 
+    "machine learning", "deep learning", "software testing and QA", "agile methodologies", 
+    "CI/CD pipelines", "database design", "programming best practices", "security in development", 
+    "mobile app development", "project management for developers", "open source contribution", 
+    "developer tools and IDEs", "documentation and code commenting", "coding interview preparation"
+]
+
+# Function to fetch chatbot completion from Groq API
+def get_response(query):
+    completion = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[{"role": "user", "content": query}],
+        temperature=0.7,
+        max_completion_tokens=1024,
+        top_p=1,
+    )
+    response = completion.choices[0].message.content
+    return response
+
+# Function to transcribe audio using Whisper model
+def transcribe_audio(file):
+    transcription = client.audio.transcriptions.create(
+        file=(file.name, file.read()),
+        model="whisper-large-v3-turbo",
+        response_format="verbose_json"
+    )
+    return transcription.text
+
+def main():
+    st.title("Programming Developer Advisor Chatbot")
+
+    # Let the user choose a developer-related topic or type a custom query
+    topic = st.selectbox("Choose a programming topic", developer_topics)
+    user_input = st.text_area("Or ask a programming-related question:", "")
+
+    # Audio file uploader
+    uploaded_file = st.file_uploader("Upload an audio file for transcription", type=["m4a", "mp3", "wav"])
+
+    # If an audio file is uploaded, transcribe it and use the transcription for querying
+    if uploaded_file is not None:
+        st.write("Transcribing the audio...")
+        transcription = transcribe_audio(uploaded_file)
+        st.write("Transcribed text:")
+        st.write(transcription)
+
+        # Use the transcribed text as the query for the chatbot if no custom query was provided
+        query = transcription if not user_input else user_input
+
+        # Get response from the chatbot based on the query
+        if query:
+            response = get_response(query)
+            st.write("### Response:")
+            st.write(response)
+    
+    # If the user provides a query (not from audio), use that directly
+    elif user_input:
+        query = user_input
+        response = get_response(query)
+        st.write("### Response:")
+        st.write(response)
+
+    # Handle unrelated queries
+    if user_input and not any(topic in user_input.lower() for topic in developer_topics):
+        st.write("Sorry, I can only answer programming-related questions.")
+
+if __name__ == "__main__":
+    main()