Update app.py

app.py

@@ -1,98 +1,53 @@
-
-import numpy as np # linear algebra
-import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
-import httpcore
-setattr(httpcore, 'SyncHTTPTransport', 'any')
-
-from googletrans import Translator
-from huggingface_hub import hf_hub_download
+#imports 
 import os
-from huggingface_hub import login
-import whisper
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
 import gradio as gr
-
-model_audio = whisper.load_model('large')
-# model.transcribe('/kaggle/input/testing/test1.wav',fp16=False,language="Hindi")['text']
-
-translator = Translator()
-def translate(audio_path):
-    options = dict(beam_size=5, best_of=5)
-    translate_options = dict(task="translate", **options)
-    result = model_audio.transcribe(audio_path,**translate_options)
-    return result["text"]
-
-
-
-# Retrieve the token from the environment variable
-secret_label = "HF_TOKEN"
-hf_token = os.getenv(secret_label)
-login(token = hf_token,add_to_git_credential=True)
-
-
-
-
-model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
-
-tokenizer = AutoTokenizer.from_pretrained(model_id , use_auth_token=hf_token)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    token=hf_token,
-    torch_dtype=torch.bfloat16,
-    device_map="auto",
+from deepgram import DeepgramClient, PrerecordedOptions
+import google.generativeai as genai
+# Initialize Deepgram Client
+DEEPGRAM_API_KEY = "3ed8d31c6d9ea6db993727870314a7a1bd43f765"
+GEMINI_API_KEY = "AIzaSyAcyXuHl46luBwTQ
+deepgram = DeepgramClient(DEEPGRAM_API_KEY)
+
+# Configure the Gemini (Google Generative AI) API
+genai.configure(api_key=GEMINI_API_KEY)
+
+# Function to transcribe audio using Deepgram
+def transcribe_audio(audio_path):
+    with open(audio_path, 'rb') as buffer_data:
+        payload = {'buffer': buffer_data}
+        options = PrerecordedOptions(
+            smart_format=True, model="nova-2", language="hi"
+        )
+        response = deepgram.listen.prerecorded.v('1').transcribe_file(payload, options)
+        
+        # Extract the transcript from the response
+        transcript = response['results']['channels'][0]['alternatives'][0]['transcript']
+        return transcript
+
+# Function to summarize the transcription using Gemini
+def summarize_text(transcript):
+    prompt = f"This is the transcription of an audio file. It can be in Hindi, English, or another language. Generate a long summary with all the points in it in Hindi:\n\n{transcript}"
     
-)
-torch.backends.cuda.enable_mem_efficient_sdp(False)
-torch.backends.cuda.enable_flash_sdp(False)
-
-
-
-
-def llama3(query):
-    prompt = query
-    messages = [
-      {"role": "system", "content": " You are a helpful assistant and you have to generate a summary of the given prompt in a really good way and length can vary but it should clearly say all important details ."},
-      {"role": "user", "content": prompt},
-  ]
-
-    input_ids = tokenizer.apply_chat_template(
-      messages,
-      add_generation_prompt=True,
-      return_tensors="pt"
-  ).to(model.device)
-
-    terminators = [
-      tokenizer.eos_token_id,
-      tokenizer.convert_tokens_to_ids("<|eot_id|>")
-  ]
-
-    outputs = model.generate(
-      input_ids,
-      max_new_tokens=1024,
-      eos_token_id=terminators,
-      do_sample=True,
-      temperature=0.6,
-      top_p=0.9,
-  )
-    response = outputs[0][input_ids.shape[-1]:]
-    return tokenizer.decode(response, skip_special_tokens=True)
-  
-     
-def eng_to_hindi(summary):
-    translated = translator.translate(summary, src='en', dest='hi')
-    return translated.text
-
-def summarize(audio_path):
+    # Use Gemini model to generate the summary
+    model = genai.GenerativeModel('models/gemini-1.5-flash')
+    response = model.generate_content(prompt)
     
-    query=translate(audio_path)
-    summary=llama3(query)
-    return eng_to_hindi(summary)
+    # Extract and return the summary
+    return response.text
+
+# Wrapper function to handle both transcription and summarization
+def process_audio(audio_path):
+    # Step 1: Transcribe the audio
+    transcript = transcribe_audio(audio_path)
+    # print(transcript)
+    # Step 2: Summarize the transcription
+    summary = summarize_text(transcript)
     
+    return summary
 
 
 iface = gr.Interface(
-    fn=summarize,
+    fn=process_audio,
     inputs=gr.Audio(sources="upload", type="filepath"),
     outputs= gr.Textbox(label="Summary"),
     title="Audio Summarization App",