Update app.py

app.py

@@ -13,7 +13,7 @@ from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
 
-# Environment and API setup
+# Load environment variables and configure API
 load_dotenv()
 os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
@@ -61,53 +61,61 @@ def main():
     st.set_page_config("Chat PDF")
     st.header("Chat with PDF using Gemini💁")
 
-    # Bokeh button to activate speech recognition
-    stt_button = Button(label="Speak", width=100)
-    stt_button.js_on_event("button_click", CustomJS(code="""
-        var recognition = new webkitSpeechRecognition();
-        recognition.continuous = true;
-        recognition.interimResults = true;
-    
-        recognition.onresult = function (e) {
-            var value = "";
-            for (var i = e.resultIndex; i < e.results.length; ++i) {
-                if (e.results[i].isFinal) {
-                    value += e.results[i][0].transcript;
+    # Organizing layout to separate PDF upload and STT
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.subheader("Upload PDF")
+        pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
+        if st.button("Process PDF"):
+            with st.spinner("Processing PDF..."):
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                get_vector_store(text_chunks)
+                st.success("PDF processing complete.")
+
+    with col2:
+        st.subheader("Voice Question")
+        # Bokeh button to activate speech recognition
+        stt_button = Button(label="Speak", width=100)
+        stt_button.js_on_event("button_click", CustomJS(code="""
+            var recognition = new webkitSpeechRecognition();
+            recognition.continuous = true;
+            recognition.interimResults = true;
+        
+            recognition.onresult = function (e) {
+                var value = "";
+                for (var i = e.resultIndex; i < e.results.length; ++i) {
+                    if (e.results[i].isFinal) {
+                        value += e.results[i][0].transcript;
+                    }
+                }
+                if (value != "") {
+                    document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
                 }
             }
-            if (value != "") {
-                document.dispatchEvent(new CustomEvent("GET_TEXT", {detail: value}));
+            recognition.onerror = function (event) {
+                console.error('Speech recognition error', event);
             }
-        }
-        recognition.start();
-        """))
-
-    # Streamlit Bokeh event for receiving transcribed text
-    result = streamlit_bokeh_events(
-        stt_button,
-        events="GET_TEXT",
-        key="listen",
-        refresh_on_update=False,
-        override_height=75,
-        debounce_time=0
-    )
+            recognition.start();
+            """))
 
-    # Process the transcribed text
-    if result:
-        if "GET_TEXT" in result:
-            user_question = result.get("GET_TEXT")
-            st.write(f"Transcribed Question: {user_question}")
-            user_input(user_question)
+        # Streamlit Bokeh event for receiving transcribed text
+        result = streamlit_bokeh_events(
+            stt_button,
+            events="GET_TEXT",
+            key="listen",
+            refresh_on_update=False,
+            override_height=75,
+            debounce_time=0
+        )
 
-    with st.sidebar:
-        st.title("Menu:")
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
+        # Process the transcribed text
+        if result:
+            if "GET_TEXT" in result:
+                user_question = result.get("GET_TEXT")
+                st.write(f"Transcribed Question: {user_question}")
+                user_input(user_question)
 
 if __name__ == "__main__":
-    main()
+    main()