Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Care Taker
-emoji: 🐨
-colorFrom: red
-colorTo: green
-sdk: gradio
-sdk_version: 4.0.2
+title: care_taker
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 3.50.2
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,149 @@
+import os
+import speech_recognition as sr
+# import pickle
+# import nltk
+# from nltk.corpus import wordnet
+# import pandas as pd
+import difflib
+import gradio as gr
+from transformers import pipeline
+import numpy as np
+
+transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
+
+
+# nltk.download('wordnet')
+
+
+
+class Model_Voice_Text():
+    
+    """
+    This class takes the voices, convert them to text
+    """
+    #open and read the file after the appending:
+
+    def __init__(self) -> None:
+        self.SR_obj = sr.Recognizer()
+        self.KEYWORDS = ['suicide', 'urgent', 'poor', 'in-need', 'old', 'pregnant', 'refugee', 'new immigrant', 'patient', 'ill', 'sick', 'anxiety', 'anxious']
+        # self.fuzzer = fuzz.Fuzz()
+    
+    # Define a function to find the number of times the word similar to the word stored in variable target_var, in a text stored in a variable named text_res
+    def find_similar_word_count(self, text, target_var):
+        """Finds the number of times the word similar to the word stored in variable target_var, in a text stored in a variable named text_res using difflib.
+
+        Args:
+            text: The text to search.
+            target_var: The word to find the similar word to.
+
+        Returns:
+            The number of times the word similar to target_var appears in the text.
+        """
+
+        # Create a list of all words in the text
+        words = text.split()
+
+        # Find all words similar to target_var
+        similar_words = difflib.get_close_matches(target_var, words, cutoff=0.75)
+
+        # Return the number of similar words
+        return len(similar_words)
+
+
+    def matching_text(self, text):
+        ret = []
+        # words = nltk.word_tokenize(text)
+        for target_var in self.KEYWORDS:
+            count = self.find_similar_word_count(text, target_var)
+            
+            # matches = process.extract(text, word)
+            if count>0:
+                ret.append((target_var, count))
+        if ret == []:
+            ret.append("nothing found")
+        
+        ret.append(text)
+        return ret
+    
+    def transcribe(self, audio):
+        # sr, y = audio
+        # y = y.astype(np.float32)
+        # y /= np.max(np.abs(y))
+
+        return transcriber(audio)["text"]
+    
+    def voice_to_text_s(self, audio):
+        # SR_obj = self.SR_obj
+        # info = sr.AudioFile(audio)
+        tran_text = self.transcribe(audio)
+        match_results = self.matching_text(tran_text.lower())
+        return match_results
+
+        # print(info)
+
+        # with info as source:
+        #     SR_obj.adjust_for_ambient_noise(source)
+        #     audio_data = SR_obj.record(source,duration=100)
+        #     result = SR_obj.recognize_google(audio_data)
+        #     match_results = self.matching_text(result)
+        #     return match_results
+
+   
+    def voice_to_text(self, voicefolder):
+        SR_obj = self.SR_obj
+        text_list = []
+        res_list = []
+
+        for subdir, dirs, files in os.walk(voicefolder):
+            for file in files:
+                print(os.path.join(subdir, file))
+                info = sr.AudioFile(os.path.join(subdir, file))
+                print(info)
+       
+                with info as source:
+                    SR_obj.adjust_for_ambient_noise(source)
+                    audio_data = SR_obj.record(source,duration=100)
+                    result = SR_obj.recognize_google(audio_data)
+                    text_list.append(result)
+                    match_results = self.matching_text(result)
+                    res_list.append([file, match_results, result])
+
+        return(text_list, res_list)
+
+
+model = Model_Voice_Text()
+
+# path = "/home/si-lab/Desktop/Projects/DataSciencePrpjects/Voice_records"
+# text, results = model.voice_to_text(path)
+
+# f = open("demofile2.txt", "a")
+# f.write(text)
+# f.close()
+# df = pd.DataFrame(results)
+# df.to_csv("list.csv", index=False)
+
+demo = gr.Blocks()
+
+
+micro_ph = gr.Interface(fn=model.voice_to_text_s,
+             inputs=gr.Audio(source="microphone", type="filepath"),
+             outputs=gr.Textbox(label="Output Box"))
+
+file_ph = gr.Interface(fn=model.voice_to_text_s,
+             inputs=gr.Audio(source="upload", type="filepath"),
+             outputs=gr.Textbox(label="Output Box"))
+
+
+with demo:
+    gr.TabbedInterface(
+        [micro_ph, file_ph],
+        ["Transcribe Microphone", "Transcribe Audio File"],
+    )
+
+demo.launch(debug=True)
+
+# pickle.dump(model, open("voice_txt.pkl", "wb"))
+
+
+
+