Update app.py

app.py

@@ -20,7 +20,6 @@ EXAMPLE_QUESTIONS = [
     "How can I make this code more readable?"
 ]
 
-# --- API CALLS ---
 def call_groq_api(prompt, model="llama3-70b-8192"):
     headers = {"Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json"}
     data = {"model": model, "messages": [{"role": "user", "content": prompt}]}
@@ -46,7 +45,6 @@ def call_blackbox_agent(messages):
     else:
         return call_groq_api(messages[-1]["content"])
 
-# --- UTILS ---
 def code_matches_language(code, language):
     if language.lower() in code.lower():
         return True
@@ -77,72 +75,12 @@ def is_coding_question(question):
     except Exception:
         return False
 
-# --- STREAMLIT APP ---
 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", "AI Workflow", "Semantic Search"])
 
-if page == "Home":
-    st.header("Welcome to the AI Assistant!")
-    st.markdown("""
-    - **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 == "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:
-        code_input = uploaded_file.read().decode("utf-8")
-        st.text_area("File content", code_input, height=200, key="file_content")
-    col1, col2, col3, col4 = st.columns(4)
-    with col1:
-        programming_language = st.selectbox("Programming Language", PROGRAMMING_LANGUAGES)
-    with col2:
-        skill_level = st.selectbox("Skill Level", SKILL_LEVELS)
-    with col3:
-        user_role = st.selectbox("Your Role", USER_ROLES)
-    with col4:
-        explanation_language = st.selectbox("Explanation Language", EXPLANATION_LANGUAGES)
-    if code_input:
-        st.caption(f"Complexity: {calculate_code_complexity(code_input)}")
-    if st.button("Run Workflow", type="primary"):
-        if not code_input.strip():
-            st.error("Please paste or upload your code.")
-        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 AI Workflow..."):
-                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}"}
-                    ])),
-                    ("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}")),
-                ]
-                timeline = []
-                for step, output in steps:
-                    timeline.append({"step": step, "output": output})
-                st.success("Workflow complete!")
-                for t in timeline:
-                    st.subheader(t["step"])
-                    st.write(t["output"])
-                st.subheader("Code Diff (Original vs Refactored)")
-                refactored_code = steps[1][1]
-                st.code(get_inline_diff(code_input, refactored_code), language=programming_language.lower())
-                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")
-
-elif page == "Semantic Search":
+if page == "Semantic Search":
     st.header("Semantic Search")
     code_input = st.text_area("Paste your code here", height=200, key="sem_code")
     uploaded_file = st.file_uploader("Or upload a code file", type=["py", "js", "ts", "java", "cpp", "cs"], key="sem_file")
@@ -162,6 +100,47 @@ elif page == "Semantic Search":
     st.caption("Example questions:")
     st.write(", ".join(EXAMPLE_QUESTIONS))
 
+    # --- Voice input button (for local use only) ---
+    st.markdown("""
+    <button id="voice-btn" style="font-size:22px; 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;
+            // Find the Streamlit input and set its value
+            const streamlitInput = window.parent.document.querySelector('input[data-testid="stTextInput"]');
+            if (streamlitInput) {
+                streamlitInput.value = transcript;
+                streamlitInput.dispatchEvent(new Event('input', { bubbles: true }));
+            }
+            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>
+    """, unsafe_allow_html=True)
+
+    # --- Single input field for question ---
     question = st.text_input("Ask a question about your code", key="sem_question")
 
     if st.button("Run Semantic Search"):