Update app.py

app.py

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+import os
+os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
+
+import torch
+from flask import Flask, request, jsonify
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Initialize the Flask app
+app = Flask(__name__)
+
+# Load the Hugging Face model and tokenizer for the Llama model
+model_name = "mattshumer/Reflection-Llama-3.1-70B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+# Sample responses based on sentiment (dummy example, can enhance based on model usage)
+responses = {
+    "positive": "I'm glad you're feeling positive! Keep up the great mood!",
+    "neutral": "It seems you're feeling neutral. I'm here if you want to talk!",
+    "negative": "I'm sorry you're feeling this way. Here's a helpline for support: 1-800-123-4567."
+}
+
+def analyze_sentiment(text):
+    """Function to analyze the sentiment of the user input."""
+    inputs = tokenizer(text, return_tensors="pt").to("cuda")
+    outputs = model.generate(inputs["input_ids"], max_length=100)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Based on generated text, let's assume it has sentiment analysis capabilities or text summarization
+    if "happy" in generated_text.lower() or "good" in generated_text.lower():
+        return "positive"
+    elif "sad" in generated_text.lower() or "bad" in generated_text.lower():
+        return "negative"
+    else:
+        return "neutral"
+
+@app.route('/chatbot', methods=['POST'])
+def chatbot():
+    """Handle user input and provide appropriate responses."""
+    user_input = request.json['input']
+    sentiment = analyze_sentiment(user_input)
+    response = responses[sentiment]
+    return jsonify({"response": response, "sentiment": sentiment})
+
+if __name__ == '__main__':
+    app.run(debug=True)