Update app.py

app.py

@@ -2,10 +2,10 @@ import streamlit as st
 import difflib
 import requests
 import datetime
-import base64
-import io
+import streamlit.components.v1 as components
 
 # --- CONFIG ---
+# Place your API keys here
 GROQ_API_KEY = st.secrets.get('GROQ_API_KEY', 'YOUR_GROQ_API_KEY')
 BLACKBOX_API_KEY = st.secrets.get('BLACKBOX_API_KEY', 'YOUR_BLACKBOX_API_KEY')
 
@@ -22,8 +22,9 @@ EXAMPLE_QUESTIONS = [
     "How can I make this code more readable?"
 ]
 
-# --- API CALLS ---
+# --- API STUBS ---
 def call_groq_api(prompt, model="llama3-70b-8192"):
+    # Replace with actual Groq API call
     headers = {"Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json"}
     data = {"model": model, "messages": [{"role": "user", "content": prompt}]}
     response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=data, headers=headers)
@@ -32,21 +33,14 @@ def call_groq_api(prompt, model="llama3-70b-8192"):
     else:
         return f"[Groq API Error] {response.text}"
 
-def call_blackbox_agent(messages, model="gpt-4o"):
-    """
-    messages: list of dicts, e.g.
-      [
-        {"role": "system", "content": "You are a helpful coding assistant."},
-        {"role": "user", "content": "Refactor this code: ..."}
-      ]
-    """
-    url = "https://api.blackbox.ai/v1/chat/completions"
+def call_blackbox_agent(messages):
+    url = "https://api.code.blackbox.ai/v1/chat/completions"
     headers = {
         "Content-Type": "application/json",
         "Authorization": f"Bearer {BLACKBOX_API_KEY}"
     }
     data = {
-        "model": model,
+        "model": "code-chat",
         "messages": messages
     }
     response = requests.post(url, headers=headers, json=data)
@@ -57,12 +51,14 @@ def call_blackbox_agent(messages, model="gpt-4o"):
 
 # --- UTILS ---
 def code_matches_language(code, language):
+    # Simple heuristic, can be improved
     if language.lower() in code.lower():
         return True
-    return True 
+    return True  # For demo, always True
 
 def calculate_code_complexity(code):
-    lines = code.count('\\n') + 1
+    # Dummy complexity metric
+    lines = code.count('\n') + 1
     return f"{lines} lines"
 
 def get_inline_diff(original, modified):
@@ -73,25 +69,40 @@ def get_inline_diff(original, modified):
         fromfile='Original',
         tofile='Refactored'
     )
-    return '\\n'.join(diff)
+    return '\n'.join(diff)
+
+def is_coding_question(question):
+    """
+    Uses Blackbox AI agent to check if the question is about programming/code.
+    Returns True if yes, False otherwise.
+    """
+    messages = [
+        {"role": "system", "content": "You are a helpful coding assistant."},
+        {"role": "user", "content": f"Is the following question about programming or code? Answer only 'yes' or 'no'. Question: {question}"}
+    ]
+    try:
+        response = call_blackbox_agent(messages)
+        return 'yes' in response.lower()
+    except Exception:
+        return False
 
 # --- STREAMLIT APP ---
-st.set_page_config(page_title="Code Workflows", layout="wide")
-st.title("CodeGenie")
+st.set_page_config(page_title="AI Workflow App", layout="wide")
+st.title("AI Assistant with Workflow (Streamlit Edition)")
 
 # Navigation
-page = st.sidebar.radio("Navigate", ["Home", "Code Workflow", "Semantic Search"])
+page = st.sidebar.radio("Navigate", ["Home", "AI Workflow", "Semantic Search", "Code Comment Generator"])
 
 if page == "Home":
-    st.header("Welcome to the Code Genie!")
+    st.header("Welcome to the AI Assistant!")
     st.markdown("""
-    - **Full Code Workflow:** Complete code analysis pipeline with explanation, refactoring, review, and testing (powered by Groq/Blackbox)
+    - **Full AI Workflow:** Complete code analysis pipeline with explanation, refactoring, review, and testing (powered by Groq/Blackbox)
     - **Semantic Search:** Ask natural language questions about your code and get intelligent answers
     """)
     st.info("Select a feature from the sidebar to get started.")
 
-elif page == "Code Workflow":
-    st.header("Full Code Workflow")
+elif page == "AI Workflow":
+    st.header("Full AI Workflow")
     code_input = st.text_area("Paste your code here", height=200)
     uploaded_file = st.file_uploader("Or upload a code file", type=["py", "js", "ts", "java", "cpp", "cs"])
     if uploaded_file:
@@ -114,7 +125,8 @@ elif page == "Code Workflow":
         elif not code_matches_language(code_input, programming_language):
             st.error(f"Language mismatch. Please check your code and language selection.")
         else:
-            with st.spinner("Running Code Workflow..."):
+            with st.spinner("Running AI Workflow..."):
+                # Simulate workflow steps
                 steps = [
                     ("Explain", call_groq_api(f"Explain this {programming_language} code for a {skill_level} {user_role} in {explanation_language}:\n{code_input}")),
                     ("Refactor", call_blackbox_agent([
@@ -132,14 +144,14 @@ elif page == "Code Workflow":
                 for t in timeline:
                     st.subheader(t["step"])
                     st.write(t["output"])
-                # Show code diff (Original vs Refactored)
+                # Show code diff (dummy for now)
                 st.subheader("Code Diff (Original vs Refactored)")
                 refactored_code = steps[1][1]  # Blackbox agent output
                 st.code(get_inline_diff(code_input, refactored_code), language=programming_language.lower())
                 # Download report
-                report = f"Code Workflow Report\\nGenerated on: {datetime.datetime.now()}\\nLanguage: {programming_language}\\nSkill Level: {skill_level}\\nRole: {user_role}\\n\\n"
+                report = f"AI Workflow Report\nGenerated on: {datetime.datetime.now()}\nLanguage: {programming_language}\nSkill Level: {skill_level}\nRole: {user_role}\n\n"
                 for t in timeline:
-                    report += f"## {t['step']}\\n{t['output']}\\n\\n---\\n\\n"
+                    report += f"## {t['step']}\n{t['output']}\n\n---\n\n"
                 st.download_button("Download Report", report, file_name="ai_workflow_report.txt")
 
 elif page == "Semantic Search":
@@ -159,29 +171,59 @@ elif page == "Semantic Search":
     with col4:
         explanation_language = st.selectbox("Explanation Language", EXPLANATION_LANGUAGES, key="sem_expl")
 
-    # Audio recorder for voice input
-    audio_bytes = st.file_uploader("Record your question (wav format)", type=["wav"], key="audio_input")
-    question = st.text_input("Ask a question about your code")
-
-    if audio_bytes is not None:
-        audio_data = audio_bytes.read()
-        audio_base64 = base64.b64encode(audio_data).decode("utf-8")
-        # Prepare message for Blackbox AI agent to transcribe audio
-        messages = [
-            {"role": "system", "content": "You are a helpful assistant that transcribes audio to text."},
-            {"role": "user", "content": f"Transcribe this audio (base64 encoded wav): {audio_base64}"}
-        ]
-        transcription = call_blackbox_agent(messages)
-        st.session_state['voice_question'] = transcription
-        st.experimental_rerun()
-
-    # Use transcribed question if available
-    if 'voice_question' in st.session_state and st.session_state['voice_question']:
-        question = st.session_state['voice_question']
-
     st.caption("Example questions:")
     st.write(", ".join(EXAMPLE_QUESTIONS))
 
+    # --- Voice input widget ---
+    if "sem_question" not in st.session_state:
+        st.session_state["sem_question"] = ""
+
+    voice_input = components.html('''
+    <button id="voice-btn" style="margin-bottom:8px;">🎤 Speak your question</button>
+    <span id="voice-status" style="margin-left:8px;"></span>
+    <script>
+    const btn = document.getElementById('voice-btn');
+    const status = document.getElementById('voice-status');
+    let recognition;
+    if ('webkitSpeechRecognition' in window) {
+        recognition = new webkitSpeechRecognition();
+        recognition.lang = 'en-US';
+        recognition.continuous = false;
+        recognition.interimResults = false;
+        btn.onclick = function() {
+            recognition.start();
+            status.textContent = 'Listening...';
+        };
+        recognition.onresult = function(event) {
+            const transcript = event.results[0][0].transcript;
+            window.parent.postMessage({isStreamlitMessage: true, type: 'streamlit:setComponentValue', value: transcript}, '*');
+            status.textContent = 'Heard: ' + transcript;
+        };
+        recognition.onerror = function() {
+            status.textContent = 'Voice error';
+        };
+        recognition.onend = function() {
+            if (status.textContent === 'Listening...') status.textContent = '';
+        };
+    } else {
+        btn.disabled = true;
+        status.textContent = 'Voice not supported';
+    }
+    </script>
+    ''', height=60)
+
+    # If voice input is received, update the question field directly in session state
+    if voice_input and isinstance(voice_input, str) and voice_input.strip():
+        if is_coding_question(voice_input):
+            st.session_state["sem_question"] = voice_input
+            st.success(f"Question recognized: {voice_input}")
+        else:
+            st.warning("Please ask a relevant question.")
+
+    # Single input field for question (typed or spoken)
+    question = st.text_input("Ask a question about your code", key="sem_question")
+
+    # Run Semantic Search button
     if st.button("Run Semantic Search"):
         if not code_input.strip() or not question.strip():
             st.error("Both code and question are required.")
@@ -192,3 +234,30 @@ elif page == "Semantic Search":
                 answer = call_groq_api(f"{question}\n\nCode:\n{code_input}")
                 st.success("Answer:")
                 st.write(answer)
+
+elif page == "Code Comment Generator":
+    st.header("Code Comment Generator")
+    code_input = st.text_area("Paste your code here", height=200, key="comment_code")
+    uploaded_file = st.file_uploader("Or upload a code file", type=["py", "js", "ts", "java", "cpp", "cs"], key="comment_file")
+    if uploaded_file:
+        code_input = uploaded_file.read().decode("utf-8")
+        st.text_area("File content", code_input, height=200, key="comment_file_content")
+    programming_language = st.selectbox("Programming Language", PROGRAMMING_LANGUAGES, key="comment_lang")
+    if st.button("Generate Comments"):
+        if not code_input.strip():
+            st.error("Please paste or upload your code.")
+        else:
+            with st.spinner("Generating commented code..."):
+                prompt = (
+                    f"Add clear, helpful comments to this {programming_language} code. "
+                    "Keep the code unchanged except for adding comments. "
+                    "Return the full code with comments:\n\n"
+                    f"{code_input}"
+                )
+                commented_code = call_blackbox_agent([
+                    {"role": "system", "content": "You are a helpful coding assistant."},
+                    {"role": "user", "content": prompt}
+                ])
+                st.success("Commented code generated!")
+                st.code(commented_code, language=programming_language.lower())
+                st.download_button("Download Commented Code", commented_code, file_name="commented_code.txt")