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app.py

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+import os
+import transformers
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
+from transformers import AutoModelForSeq2SeqLM, pipeline
+from huggingface_hub import login
+import gradio as gr
+import numpy as np
+
+new_model = "tensorgirl/finetuned-gemma"
+model = AutoModelForCausalLM.from_pretrained(new_model, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(new_model, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
+
+generator = transformers.pipeline(
+        "text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True,
+        device_map="auto",
+    )
+
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M")
+tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
+device = 0 if torch.cuda.is_available() else -1
+
+def translate(text, src_lang, tgt_lang):
+
+    translation_pipeline = pipeline("translation", model=model, tokenizer=tokenizer, src_lang=src_lang, tgt_lang=tgt_lang, max_length=400, device=device)
+    result = translation_pipeline(text)
+    return result[0]['translation_text']
+
+def English(audio):
+
+    transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
+    sr, y = audio
+    y = y.astype(np.float32)
+    y = np.max(np.abs(y))
+
+    return transcriber({"sampling_rate": sr, "raw": y})["text"]
+
+def Hindi(audio):
+
+    transcriber = pipeline("automatic-speech-recognition", model="theainerd/Wav2Vec2-large-xlsr-hindi")
+    sr, y = audio
+    y = y.astype(np.float32)
+    y = np.max(np.abs(y))
+
+    text = transcriber({"sampling_rate":sr, "raw":y})["text"]
+
+    return translate(text, "hin_Deva", "eng_Latn")
+
+
+def Telegu(audio):
+
+    transcriber = pipeline("automatic-speech-recognition", model="anuragshas/wav2vec2-large-xlsr-53-telugu")
+    sr, y = audio
+    y = y.astype(np.float32)
+    y = np.max(np.abs(y))
+
+    text = transcriber({"sampling_rate":sr, "raw":y})["text"]
+
+    return translate(text, "tel_Telu", "eng_Latn")
+
+def Tamil(audio):
+
+    transcriber = pipeline("automatic-speech-recognition", model="Harveenchadha/vakyansh-wav2vec2-tamil-tam-250")
+    sr, y = audio
+    y = y.astype(np.float32)
+    y = np.max(np.abs(y))
+
+    text = transcriber({"sampling_rate":sr, "raw":y})["text"]
+
+    return translate(text, "tam_Taml", "eng_Latn")
+
+def Kannada(audio):
+
+    transcriber = pipeline("automatic-speech-recognition", model="vasista22/whisper-kannada-medium")
+    sr, y = audio
+    y = y.astype(np.float32)
+    y = np.max(np.abs(y))
+
+    text = transcriber({"sampling_rate":sr, "raw":y})["text"]
+
+    return translate(text, "kan_Knda", "eng_Latn")
+
+def predict(audio, language):
+
+    if language == English:
+        message = English(audio)
+
+    if language == Hindi:
+        message = Hindi(audio)
+
+    if language == Telegu:
+        message = Telegu(audio)
+
+    if language == Tamil:
+        message = Tamil(audio)
+
+    if language == Kannada:
+        message = Kannada(audio)
+
+    print(message)
+
+    sequences = generator(
+            message,
+            max_length=200,
+            do_sample=False,
+            top_k=10,
+            num_return_sequences=1,
+            eos_token_id=tokenizer.eos_token_id,)
+
+    answer = ""
+    for seq in sequences:
+            answer = answer + seq['generated_text'] + " "
+
+    print(answer)
+    if language == English:
+        return answer
+
+    if language == Hindi:
+        return translate(text,eng_Latn, hin_Deva)
+
+    if language == Telegu:
+        return translate(text,eng_Latn, tel_Telu)
+
+    if language == Tamil:
+        return translate(text, eng_Latn, tam_Taml)
+
+    if language == Kannada:
+        return translate(text, eng_Latn, kan_Knda)
+
+    return answer
+
+demo = gr.Interface(
+    predict,
+    [gr.Audio(),
+    gr.Dropdown(
+            ["Hindi", "Telegu", "Tamil", "Kannada", "English"], label="Language", info="Please select language of your choice"
+        )],
+    "text",
+    title = "Farmers-Helper-Bot",
+    description = "Ask your queries in your regional Language",
+    theme=gr.themes.Soft()
+)
+
+demo.launch(share=True)