Flask pp

Dockerfile

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+# Use the official Python image from Docker Hub
+FROM python:3.9
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Copy the requirements file into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install the Python dependencies
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Copy the current directory contents into the container at /code
+COPY . .
+
+# Define the command to start the Flask app using Gunicorn
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "main:app"]

main.py

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+from flask import Flask, jsonify, request
+import requests
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+app = Flask(__name__)
+
+# Initialize sentiment analysis model
+sentiment_tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
+sentiment_model = AutoModelForSeq2SeqLM.from_pretrained("mrm8488/t5-base-finetuned-emotion")
+
+# Initialize dialogue generation model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
+model = AutoModelForSeq2SeqLM.from_pretrained("microsoft/GODEL-v1_1-large-seq2seq")
+
+# Last.fm API key
+API_KEY = "e554f25da26e93055f2780bbe2b9293b"
+
+# Function to generate response
+def generate_response(dialog):
+    knowledge = ''
+    instruction = f'Instruction: given a dialog context, you need to respond empathically.'
+    dialog_text = ' EOS '.join(dialog)
+    query = f"{instruction} [CONTEXT] {dialog_text} {knowledge}"
+
+    input_ids = tokenizer.encode(query, return_tensors="pt")
+    output = model.generate(input_ids, max_length=16, min_length=2, top_p=0.9, do_sample=True)
+    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    return generated_text
+
+# Function to perform sentiment analysis
+def sentiment_finder(user_dialog):
+    input_ids = sentiment_tokenizer.encode(user_dialog + '</s>', return_tensors='pt')
+    output = sentiment_model.generate(input_ids=input_ids, max_length=2)
+    emotion = [sentiment_tokenizer.decode(ids) for ids in output][0]
+    return emotion[6:]
+
+@app.route("/get_response", methods=["POST", "GET"])
+def get_response():
+    data = request.json
+    dialog = data.get('dialog', [])
+    generated_text = generate_response(dialog)
+    user_dialog = dialog[-1]
+    emotion = sentiment_finder(user_dialog)
+
+    # Fetch music recommendations based on emotion
+    recommendations_url = f"http://ws.audioscrobbler.com/2.0/?method=tag.gettoptracks&tag={emotion}&api_key={API_KEY}&format=json&limit=4"
+    recommendations_response = requests.get(recommendations_url)
+
+    recommendations = []
+    if recommendations_response.ok:
+        recommendations_data = recommendations_response.json()
+        recommendations = recommendations_data["tracks"]["track"]
+
+    response_data = {'generated_response': generated_text, 'recommendations': recommendations}
+    return jsonify(response_data)
+
+if __name__ == '__main__':
+    app.run(port=8000)

requirements.txt

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+transformers[sentencepiece]
+flask~=2.2.2
+flask-restful
+flask-cors
+streamlit
+requests
+torch
+torchvision
+torchaudio
+pyttsx3
+PyAudio
+SpeechRecognition
+setuptools
+gunicorn