app1

app.py

@@ -121,7 +121,6 @@ qa_chain = ConversationalRetrievalChain.from_llm(
 
 import numpy as np
 import soundfile as sf
-
 # Load ASR pipeline
 transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-large")
 
@@ -145,14 +144,32 @@ def chat_interface(question, audio_input=None, history=None):
         # Transcribe the audio input
         question = transcribe(audio_input)
 
-    # Original chatbot logic goes here
-    return question
+
+        return question
+
+    # Original chatbot logic
+    result = qa_chain.invoke({'question': question})
+    output_string = result['output']
+
+    # Find the index of the last occurrence of "answer": in the string
+    answer_index = output_string.rfind('"answer":')
+
+    # Extract the substring starting from the "answer": index
+    answer_part = output_string[answer_index + len('"answer":'):].strip()
+
+    # Find the next occurrence of a double quote to get the start of the answer value
+    quote_index = answer_part.find('"')
+
+    # Extract the answer value between double quotes
+    answer_value = answer_part[quote_index + 1:answer_part.find('"', quote_index + 1)]
+
+    return answer_value
 
 chatbot_gradio_app = gr.Interface(
     fn=chat_interface,
     inputs=[
         gr.Textbox(lines=3, label="Type your message here"),
-        gr.Audio(label="Record your voice", type='numpy')
+        gr.Audio(label="Record your voice", type='numpy')  # Change type to "microphone"
     ],
     outputs=gr.Textbox(label="Bot's Response"),
 )