Added application

README.md

@@ -1,12 +1,32 @@
 ---
-title: Meeting Minute Generator And Question And Answer Chatbot
-emoji: 😻
-colorFrom: yellow
-colorTo: red
+title: Meeting Minutes Generator and Question Answer Application
+emoji: 👀
+colorFrom: pink
+colorTo: pink
 sdk: gradio
-sdk_version: 4.36.1
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## Overview
+
+This application takes an MP3 file of a meeting recording in English, converts it into text, generates meeting minutes in a specified format, and answers questions based on the minutes using OpenAI APIs, ChatGPT, and the LangChain framework.
+
+## Features
+
+- **Transcription**: Converts audio recordings (MP3) to text.
+- **Meeting Minutes Generation**: Summarizes the transcription into meeting minutes.
+- **Question and Answer**: Allows users to ask questions based on the meeting minutes.
+
+## Prerequisites
+
+Before you begin, ensure you have met the following requirements:
+
+- Python 3.7 or higher
+- An OpenAI API key
+- The following Python libraries:
+  - `openai`
+  - `transformers`
+  - `langchain`
+  - `pydub`
+  - `whisper`

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+from transformers import pipeline
+import torchaudio
+import time
+
+# Load Whisper ASR model
+transcriber = pipeline(model="openai/whisper-base")
+
+# Load summarization model
+summarization_model = pipeline("summarization")
+
+# Load question-answering model
+model_name = "deepset/roberta-base-squad2"
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+
+def translate_audio(audio):
+    # Step 1: Transcribe audio to text
+    transcription = transcriber(audio)
+    print('transcription', transcription)
+
+    # Step 2: Translate text to Hindi
+    summary = summarization_model(transcription['text'])
+    print('summary', summary)
+
+    return transcription['text'], summary[0]['summary_text']
+
+def answer_question(context, question):
+    QA_input = {
+        'question': question,
+        'context': context
+    }
+    print('----QA_input----', QA_input)
+    return nlp(QA_input)['answer']
+
+# Create Gradio interface
+with gr.Blocks() as iface:
+    gr.Markdown("# Audio Translator, Summarizer, and QA System")
+
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="Upload Audio")
+        transcription_output = gr.Textbox(
+            label="Transcribed Text", 
+            info="Initial text")
+        translation_output = gr.Textbox(
+            label="Summary", 
+            info="Meeting minute")
+
+    translate_button = gr.Button("Translate Audio")
+
+    translate_button.click(
+        translate_audio,
+        inputs=[audio_input],
+        outputs=[transcription_output, translation_output]
+    )
+
+    def respond(message, chat_history, context):
+      bot_message = answer_question(context, message)
+      print('----bot_message---', bot_message)
+      chat_history.append((message, bot_message))
+      time.sleep(2)
+      return "", chat_history
+
+    with gr.Blocks() as demo:
+      chatbot = gr.Chatbot()
+      msg = gr.Textbox()
+      clear = gr.ClearButton([msg, chatbot])
+
+      msg.submit(respond, [msg, chatbot, transcription_output], [msg, chatbot])
+
+# Launch the app
+iface.launch(share=True)  # 'share=True' to get a public link 

requirements.txt

@@ -0,0 +1,4 @@
+transformers
+torchaudio
+gradio
+sentencepiece