Update app.py

app.py

@@ -2,8 +2,10 @@ import streamlit as st
 import difflib
 import requests
 import datetime
+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')
 
@@ -20,8 +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)
@@ -30,38 +33,32 @@ 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)
     if response.status_code == 200:
         return response.json()["choices"][0]["message"]["content"]
     else:
+        # fallback to Groq if Blackbox fails
         return call_groq_api(messages[-1]["content"])
 
-
-
 # --- 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):
+    # Dummy complexity metric
     lines = code.count('\n') + 1
     return f"{lines} lines"
 
@@ -75,23 +72,38 @@ def get_inline_diff(original, modified):
     )
     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"])
 
 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:
@@ -114,13 +126,14 @@ 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([
-    {"role": "system", "content": "You are a helpful coding assistant."},
-    {"role": "user", "content": f"Refactor this {programming_language} code: {code_input}"}
-])),
+                        {"role": "system", "content": "You are a helpful coding assistant."},
+                        {"role": "user", "content": f"Refactor this {programming_language} code: {code_input}"}
+                    ])),
                     ("Review", call_groq_api(f"Review this {programming_language} code for errors and improvements: {code_input}")),
                     ("ErrorDetection", call_groq_api(f"Find bugs in this {programming_language} code: {code_input}")),
                     ("TestGeneration", call_groq_api(f"Generate tests for this {programming_language} code: {code_input}")),
@@ -137,7 +150,7 @@ elif page == "Code Workflow":
                 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"
                 st.download_button("Download Report", report, file_name="ai_workflow_report.txt")
@@ -158,82 +171,76 @@ elif page == "Semantic Search":
         user_role = st.selectbox("Your Role", USER_ROLES, key="sem_role")
     with col4:
         explanation_language = st.selectbox("Explanation Language", EXPLANATION_LANGUAGES, key="sem_expl")
-    import streamlit.components.v1 as components
-
-    # Initialize session state variables
-    if "voice_question" not in st.session_state:
-        st.session_state.voice_question = ""
-    if "auto_run_search" not in st.session_state:
-        st.session_state.auto_run_search = False
-
-    # Layout: question input and mic button side by side
-    col_question, col_mic = st.columns([9,1])
-    with col_question:
-        question = st.text_input("Ask a question about your code", value=st.session_state.voice_question, key="question_input")
-    with col_mic:
-        # Microphone button and JS for voice recognition embedded via components.html
-        components.html(
-            """
-            <button id="mic-btn" style="height:38px; width:38px; font-size:20px;">🎤</button>
-            <script>
-            const micBtn = document.getElementById('mic-btn');
-            const SpeechRecognition = window.SpeechRecognition || window.webkitSpeechRecognition;
-            if (!SpeechRecognition) {
-                alert("Speech Recognition not supported in this browser.");
-                micBtn.disabled = true;
-            } else {
-                const recognition = new SpeechRecognition();
-                recognition.lang = 'en-US';
-                recognition.interimResults = false;
-                recognition.maxAlternatives = 1;
-
-                micBtn.onclick = () => {
-                    recognition.start();
-                    micBtn.textContent = '🎙️';
-                };
-
-                recognition.onresult = (event) => {
-                    const transcript = event.results[0][0].transcript;
-                    // Send transcript to Streamlit by updating the input field
-                    const inputBox = window.parent.document.querySelector('input[data-key="question_input"]');
-                    if (inputBox) {
-                        inputBox.value = transcript;
-                        inputBox.dispatchEvent(new Event('input', { bubbles: true }));
-                    }
-                    micBtn.textContent = '🎤';
-                };
-
-                recognition.onerror = (event) => {
-                    console.error('Speech recognition error', event.error);
-                    micBtn.textContent = '🎤';
-                };
-            }
-            </script>
-            """,
-            height=50,
-            scrolling=False,
-        )
-
-    # Update session state voice_question if question input changed manually or by voice
-    if question != st.session_state.voice_question:
-        st.session_state.voice_question = question
-        st.session_state.auto_run_search = True
-
-    # Automatically run semantic search if flag is set
-    if st.session_state.auto_run_search:
-        st.session_state.auto_run_search = False
-        if not code_input.strip() or not st.session_state.voice_question.strip():
-            st.error("Both code and question are required.")
-        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 Semantic Search..."):
-                answer = call_groq_api(f"{st.session_state.voice_question}\n\nCode:\n{code_input}")
-                st.success("Answer:")
-                st.write(answer)
 
-    # Manual button fallback
-    if st.button("Run Semantic Search"):
+    st.caption("Example questions:")
+    st.write(", ".join(EXAMPLE_QUESTIONS))
+
+    # Session state for question and trigger
+    if 'voice_question' not in st.session_state:
+        st.session_state['voice_question'] = ''
+    if 'run_semantic_search' not in st.session_state:
+        st.session_state['run_semantic_search'] = False
+
+    # Voice input widget (Web Speech API)
+    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)
+
+    # --- Main question input ---
+    question = st.text_input("Ask a question about your code", value=st.session_state.get('voice_question', ''), key="sem_question")
+
+    # If voice input is received, validate and set question
+    # NOTE: Streamlit's JS->Python communication for custom components is limited.
+    # For a production app, use a robust Streamlit component for voice input.
+    # Here, we simulate the process using session_state for demonstration.
+    # You may need to use streamlit_js_eval or a similar package for real-time JS->Python value passing.
+
+    # Simulate voice input: check if the question was updated by voice
+    if st.session_state.get('voice_question', '') and not st.session_state.get('run_semantic_search', False):
+        voice_input = st.session_state['voice_question']
+        if is_coding_question(voice_input):
+            st.session_state['run_semantic_search'] = True
+            st.success(f"Question recognized: {voice_input}")
+        else:
+            st.warning("Please ask a relevant question.")
+            st.session_state['voice_question'] = ''  # reset
+
+    # Run Semantic Search button
+    run_btn = st.button("Run Semantic Search")
+    # If triggered by voice or button
+    run_search = run_btn or st.session_state.get('run_semantic_search', False)
+    if run_search:
+        st.session_state['run_semantic_search'] = False  # reset trigger
         if not code_input.strip() or not question.strip():
             st.error("Both code and question are required.")
         elif not code_matches_language(code_input, programming_language):
@@ -242,4 +249,4 @@ elif page == "Semantic Search":
             with st.spinner("Running Semantic Search..."):
                 answer = call_groq_api(f"{question}\n\nCode:\n{code_input}")
                 st.success("Answer:")
-                st.write(answer)
+                st.write(answer)