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nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import torch\n", + "from torch.utils.data import DataLoader, Dataset\n", + "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, AdamW, get_linear_schedule_with_warmup\n", + "from sklearn.model_selection import train_test_split\n", + "from tqdm import tqdm\n", + "from pydub import AudioSegment\n", + "from gtts import gTTS\n", + "import openai\n", + "import speech_recognition as sr\n", + "import numpy as np\n", + "import os\n", + "\n", + "# Set your OpenAI API key\n", + "openai.api_key = \"sk-proj-Hosg4gZzlwor4O6CJtevT3BlbkFJ6WrunCU6UjU3Uqy0LW8f\"\n", + "\n", + "# Load the dataset\n", + "data = pd.read_csv(\"/content/Medicine_Details.csv\")\n", + "\n", + "def preprocess_data(data):\n", + " # Assuming all medicines are \"Excellent\" for simplicity\n", + " data['Label'] = 0\n", + " return data[['Medicine Name', 'Composition', 'Uses', 'Side_effects', 'Manufacturer', 'Label']]\n", + "\n", + "data = preprocess_data(data)\n", + "\n", + "class MedicineDataset(Dataset):\n", + " def __init__(self, data, tokenizer, max_len=128):\n", + " self.data = data\n", + " self.tokenizer = tokenizer\n", + " self.max_len = max_len\n", + "\n", + " def __len__(self):\n", + " return len(self.data)\n", + "\n", + " def __getitem__(self, idx):\n", + " row = self.data.iloc[idx]\n", + " text = f\"{row['Composition']} {row['Uses']} {row['Side_effects']} {row['Manufacturer']}\"\n", + " inputs = self.tokenizer.encode_plus(\n", + " text,\n", + " add_special_tokens=True,\n", + " max_length=self.max_len,\n", + " padding='max_length',\n", + " return_attention_mask=True,\n", + " return_tensors='pt',\n", + " truncation=True\n", + " )\n", + " return {\n", + " 'input_ids': inputs['input_ids'].flatten(),\n", + " 'attention_mask': inputs['attention_mask'].flatten(),\n", + " 'labels': torch.tensor(row['Label'], dtype=torch.long)\n", + " }\n", + "\n", + "# Split the data\n", + "train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)\n", + "\n", + "# Load tokenizer\n", + "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n", + "\n", + "# Create datasets\n", + "train_dataset = MedicineDataset(train_data, tokenizer)\n", + "test_dataset = MedicineDataset(test_data, tokenizer)\n", + "\n", + "# Create data loaders\n", + "train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)\n", + "test_loader = DataLoader(test_dataset, batch_size=16)\n", + "\n", + "# Load model\n", + "model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=3)\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device)\n", + "\n", + "# Set up optimizer, scheduler, and loss function\n", + "optimizer = AdamW(model.parameters(), lr=5e-5)\n", + "total_steps = len(train_loader) * 3 # 3 epochs\n", + "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=total_steps)\n", + "\n", + "# Training loop\n", + "num_epochs = 3\n", + "gradient_accumulation_steps = 2\n", + "early_stopping_patience = 2\n", + "best_loss = float('inf')\n", + "patience_counter = 0\n", + "\n", + "for epoch in range(num_epochs):\n", + " model.train()\n", + " running_loss = 0.0\n", + " for i, batch in enumerate(tqdm(train_loader)):\n", + " inputs, labels = batch['input_ids'].to(device), batch['labels'].to(device)\n", + " attention_mask = batch['attention_mask'].to(device)\n", + "\n", + " outputs = model(input_ids=inputs, attention_mask=attention_mask, labels=labels)\n", + " loss = outputs.loss / gradient_accumulation_steps\n", + " loss.backward()\n", + "\n", + " if (i + 1) % gradient_accumulation_steps == 0:\n", + " optimizer.step()\n", + " scheduler.step()\n", + " optimizer.zero_grad()\n", + "\n", + " running_loss += loss.item() * gradient_accumulation_steps\n", + "\n", + " avg_loss = running_loss / len(train_loader)\n", + " print(f\"Epoch {epoch + 1}, Loss: {avg_loss}\")\n", + "\n", + " # Early stopping\n", + " model.eval()\n", + " val_loss = 0.0\n", + " with torch.no_grad():\n", + " for batch in test_loader:\n", + " inputs, labels = batch['input_ids'].to(device), batch['labels'].to(device)\n", + " attention_mask = batch['attention_mask'].to(device)\n", + "\n", + " outputs = model(input_ids=inputs, attention_mask=attention_mask, labels=labels)\n", + " loss = outputs.loss\n", + " val_loss += loss.item()\n", + "\n", + " val_loss /= len(test_loader)\n", + " print(f\"Validation Loss: {val_loss}\")\n", + "\n", + " if val_loss < best_loss:\n", + " best_loss = val_loss\n", + " patience_counter = 0\n", + " else:\n", + " patience_counter += 1\n", + "\n", + " if patience_counter >= early_stopping_patience:\n", + " print(\"Early stopping triggered.\")\n", + " break\n", + "\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 561, + "referenced_widgets": [ + "48a76c68ca374645980329777f5efdc3", + "de6e0636c1724086a70e76aa1a7b9f37", + "4ad333ede84545ff825b370c10254812", + "7e7b30b9c3424f0fb6c1b5b1e17e37e9", + "497aae416c3744c097b5f4e20980c21a", + "fe054fd107a148d99c28d5447ef2da15", + "91bfa2ae07c546089a264a4d62ee4c71", + "ed0f648fd5834bd4af38da225d60177c", + "4db3ef8cabed415691deaa6b240f6134", + "ead5a02848ae494395635c93d3fcb0b9", + "20bd1737ecfc4c16b97a006b2f14e6cb", + "56956a306aa140b98f49870d03c44a4c", + "49aee962ec7b4da28a06b95dedddc22c", + "2be9241055b24ce4bfebf3260f2861ec", + "a2a356f21f4f47ffacb4fa17782cdbc7", + "97d634b467cc4b758bc787078c0c535e", + "aec9bd122216436baec9fc5f1443cb49", + "9138a92bef2e4c628455096b0550e49d", + "40cae393c7bd402e81037fc8dc933d8b", + "027dab170d024a4ba57448d1988c84e7", + "aecb4bc8d2b847bab696e4e4e297d148", + "0e07c7b1c85045f2a4ce4044e21250e6", + "4f2dd7d15f664b62a6415bfe80c73dfc", + "227ef92a0aeb474fbbfbd0ebdda5ddf9", + 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"5796862e59de4c789658cae2e88a32fd", + "d70ff1b47ae04627843bfff486403e0e", + "da02ba6d7eed47e486e93a30e5997626", + "cddb71e762e14d2f820f6d46d1950d15", + "55811a18ddcc46718b6112ac2be24f2a" + ] + }, + "id": "B4SkVs-Myl1u", + "outputId": "4561150a-fc65-4d80-a04b-494404a56e42" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { 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/usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.27->gtts) (2024.6.2)\n", + "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n", + "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import torch\n", + "from torch.utils.data import DataLoader, Dataset\n", + "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, AdamW, get_linear_schedule_with_warmup\n", + "from sklearn.model_selection import train_test_split\n", + "from tqdm import tqdm\n", + "from pydub import AudioSegment\n", + "from pydub.playback import play\n", + "from gtts import gTTS\n", + "import openai\n", + "import os" + ], + "metadata": { + "id": "fRhejtLI0x7Y" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from IPython.display import HTML, Audio\n", + "from google.colab.output import eval_js\n", + "from base64 import b64decode\n", + "import numpy as np\n", + "from scipy.io.wavfile import read as wav_read\n", + "import io\n", + "import ffmpeg\n", + "\n", + "AUDIO_HTML = \"\"\"\n", + "\n", + "\"\"\"\n", + "\n", + "def get_audio():\n", + " display(HTML(AUDIO_HTML))\n", + " data = eval_js(\"data\")\n", + " binary = b64decode(data.split(',')[1])\n", + "\n", + " process = (ffmpeg\n", + " .input('pipe:0')\n", + " .output('pipe:1', format='wav')\n", + " .run_async(pipe_stdin=True, pipe_stdout=True, pipe_stderr=True, quiet=True, overwrite_output=True)\n", + " )\n", + " output, err = process.communicate(input=binary)\n", + "\n", + " riff_chunk_size = len(output) - 8\n", + " # Break up the chunk size into four bytes, held in b.\n", + " q = riff_chunk_size\n", + " b = []\n", + " for i in range(4):\n", + " q, r = divmod(q, 256)\n", + " b.append(r)\n", + "\n", + " # Replace bytes 4:8 in proc.stdout with the actual size of the RIFF chunk.\n", + " riff = output[:4] + bytes(b) + output[8:]\n", + "\n", + " sr, audio = wav_read(io.BytesIO(riff))\n", + "\n", + " return audio, sr\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "import speech_recognition as sr\n", + "from gtts import gTTS\n", + "from pydub import AudioSegment\n", + "from pydub.playback import play\n", + "import pandas as pd\n", + "\n", + "\n", + "\n", + "# Answering questions\n", + "def answer_question(medicine_name, question, medicine_info):\n", + " medicine_name = medicine_name.lower().strip()\n", + " if medicine_name not in medicine_info:\n", + " return \"Medicine not found.\"\n", + "\n", + " info = medicine_info[medicine_name]\n", + " if \"use\" in question.lower():\n", + " return info.get('Uses', 'No information available.')\n", + " elif \"side effect\" in question.lower():\n", + " return info.get('Side_effects', 'No information available.')\n", + " else:\n", + " return \"I can only answer questions about uses and side effects.\"\n", + "\n", + "# Convert text to speech and save to file\n", + "def text_to_speech(text, filename=\"response.mp3\"):\n", + " tts = gTTS(text=text, lang='en')\n", + " tts.save(filename)\n", + " return filename\n", + "\n", + "\n", + "\n", + "\n", + "# Play audio function\n", + "def play_audio(audio_data, sample_rate):\n", + " display(Audio(data=audio_data, rate=sample_rate))\n", + "\n", + "\n", + "\n", + "\n", + "def speech_to_text(audio_data, sample_rate):\n", + " recognizer = sr.Recognizer()\n", + " audio_data = audio_data.astype('int16').tobytes()\n", + " audio_data_obj = sr.AudioData(audio_data, sample_rate=sample_rate, sample_width=2)\n", + "\n", + " try:\n", + " text = recognizer.recognize_google(audio_data_obj, language='en-US', show_all=False)\n", + " return text\n", + " except sr.UnknownValueError:\n", + " return \"Sorry, I could not understand the audio.\"\n", + " except sr.RequestError:\n", + " return \"Sorry, my speech recognition service is currently unavailable.\"\n", + "\n", + "sample_rate = 48000\n", + "# Example usage\n", + "if __name__ == \"__main__\":\n", + " # Convert all values in the 'Medicine Name' column to lowercase\n", + " data['Medicine Name'] = data['Medicine Name'].str.lower()\n", + "\n", + " # Convert the data to a dictionary\n", + " medicine_info = data.set_index('Medicine Name').T.to_dict()\n", + "\n", + " # Record user's question\n", + " medicine_name_aud, sr1 = get_audio()\n", + " question_text_aud, sr2= get_audio()\n", + "\n", + " # Get the answer from the chatbot\n", + " medicine_name = speech_to_text(medicine_name_aud, sample_rate=sample_rate) # Replace with appropriate medicine name\n", + " question_text = speech_to_text(question_text_aud, sample_rate=sample_rate)\n", + " print (f\"Medicine: {medicine_name}\")\n", + " print(f\"User Question: {question_text}\")\n", + " answer_text = answer_question(medicine_name, question_text, medicine_info)\n", + " print(f\"Chatbot Answer: {answer_text}\")\n", + "\n", + " # Convert the chatbot answer to speech\n", + " tts_output_file = text_to_speech(answer_text)\n", + " print(f\"Audio response saved to {tts_output_file}.\")\n", + "\n", + " # Play the audio response\n", + " play_audio(tts_output_file, sample_rate=sample_rate)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 338 + }, + "id": "RaBjTO_TcccI", + "outputId": "2258d927-18c9-4dc3-def0-43a5d86d0bd2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":178: UserWarning: DataFrame columns are not unique, some columns will be omitted.\n", + " medicine_info = data.set_index('Medicine Name').T.to_dict()\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": 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Injection \n", + "1 Augmentin 625 Duo Tablet \n", + "2 Azithral 500 Tablet \n", + "3 Ascoril LS Syrup \n", + "4 Aciloc 150 Tablet \n", + "\n", + " Composition \\\n", + "0 Bevacizumab (400mg) \n", + "1 Amoxycillin (500mg) + Clavulanic Acid (125mg) \n", + "2 Azithromycin (500mg) \n", + "3 Ambroxol (30mg/5ml) + Levosalbutamol (1mg/5ml)... \n", + "4 Ranitidine (150mg) \n", + "\n", + " Uses \\\n", + "0 Cancer of colon and rectum Non-small cell lun... \n", + "1 Treatment of Bacterial infections \n", + "2 Treatment of Bacterial infections \n", + "3 Treatment of Cough with mucus \n", + "4 Treatment of Gastroesophageal reflux disease (... \n", + "\n", + " Side_effects \\\n", + "0 Rectal bleeding Taste change Headache Noseblee... \n", + "1 Vomiting Nausea Diarrhea Mucocutaneous candidi... \n", + "2 Nausea Abdominal pain Diarrhea \n", + "3 Nausea Vomiting Diarrhea Upset stomach Stomach... \n", + "4 Headache Diarrhea Gastrointestinal disturbance \n", + "\n", + " Manufacturer Label \n", + "0 Roche Products India Pvt Ltd 0 \n", + "1 Glaxo SmithKline Pharmaceuticals Ltd 0 \n", + "2 Alembic Pharmaceuticals Ltd 0 \n", + "3 Glenmark Pharmaceuticals Ltd 0 \n", + "4 Cadila Pharmaceuticals Ltd 0 " + ], + "text/html": [ + "\n", + "
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Medicine NameCompositionUsesSide_effectsManufacturerLabel
0Avastin 400mg InjectionBevacizumab (400mg)Cancer of colon and rectum Non-small cell lun...Rectal bleeding Taste change Headache Noseblee...Roche Products India Pvt Ltd0
1Augmentin 625 Duo TabletAmoxycillin (500mg) + Clavulanic Acid (125mg)Treatment of Bacterial infectionsVomiting Nausea Diarrhea Mucocutaneous candidi...Glaxo SmithKline Pharmaceuticals Ltd0
2Azithral 500 TabletAzithromycin (500mg)Treatment of Bacterial infectionsNausea Abdominal pain DiarrheaAlembic Pharmaceuticals Ltd0
3Ascoril LS SyrupAmbroxol (30mg/5ml) + Levosalbutamol (1mg/5ml)...Treatment of Cough with mucusNausea Vomiting Diarrhea Upset stomach Stomach...Glenmark Pharmaceuticals Ltd0
4Aciloc 150 TabletRanitidine (150mg)Treatment of Gastroesophageal reflux disease (...Headache Diarrhea Gastrointestinal disturbanceCadila Pharmaceuticals Ltd0
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