Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import difflib
 import requests
 import datetime
+import streamlit.components.v1 as components
 
 # --- CONFIG ---
 GROQ_API_KEY = st.secrets.get('GROQ_API_KEY', 'YOUR_GROQ_API_KEY')
@@ -20,6 +21,7 @@ EXAMPLE_QUESTIONS = [
     "How can I make this code more readable?"
 ]
 
+# --- API STUBS ---
 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}]}
@@ -45,12 +47,15 @@ def call_blackbox_agent(messages):
     else:
         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"
 
@@ -75,12 +80,72 @@ 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 == "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":
     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")
@@ -100,9 +165,14 @@ if 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>
+    # --- Voice input widget ---
+    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 = 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');
@@ -119,12 +189,7 @@ if page == "Semantic Search":
         };
         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 }));
-            }
+            window.parent.postMessage({isStreamlitMessage: true, type: 'streamlit:setComponentValue', value: transcript}, '*');
             status.textContent = 'Heard: ' + transcript;
         };
         recognition.onerror = function() {
@@ -138,78 +203,32 @@ if page == "Semantic Search":
         status.textContent = 'Voice not supported';
     }
     </script>
-    """, unsafe_allow_html=True)
+    ''', height=60)
+
+    # Use the return value from components.html as the transcript
+    if voice_input and isinstance(voice_input, str) and voice_input.strip():
+        if is_coding_question(voice_input):
+            st.session_state['voice_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.")
 
-    # --- Single input field for question ---
-    question = st.text_input("Ask a question about your code", key="sem_question")
+    # --- Main question input ---
+    question = st.text_input("Ask a question about your code", value=st.session_state.get('voice_question', ''), key="sem_question")
 
-    if st.button("Run Semantic Search"):
+    # Run Semantic Search button
+    run_btn = st.button("Run Semantic Search")
+    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):
             st.error(f"Language mismatch. Please check your code and language selection.")
-        elif not is_coding_question(question):
-            st.warning("Please ask a relevant question.")
         else:
             with st.spinner("Running Semantic Search..."):
                 answer = call_groq_api(f"{question}\n\nCode:\n{code_input}")
                 st.success("Answer:")
                 st.write(answer)
-
-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")
-
-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.")
+// ... existing code ...