first commit

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import logging
+import os
+
+MIN_TRANSFORMERS_VERSION = '4.25.1'
+
+# Check transformers version
+import transformers
+assert transformers.__version__ >= MIN_TRANSFORMERS_VERSION, f'Please upgrade transformers to version {MIN_TRANSFORMERS_VERSION} or higher.'
+
+# Initialize tokenizer and model from local directory
+model_dir = "./"
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16)
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS
+
+logging.basicConfig(level=logging.DEBUG)
+
+def generate_response(prompt):
+    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)
+    input_length = inputs.input_ids.shape[1]
+    outputs = model.generate(
+        **inputs, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.7, top_k=50, return_dict_in_generate=True
+    )
+    token = outputs.sequences[0, input_length:]
+    output_str = tokenizer.decode(token, skip_special_tokens=True)
+    return output_str
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    logging.debug("Received a POST request")
+    data = request.json
+    logging.debug(f"Request data: {data}")
+    user_input = data.get("message", "")
+    prompt = f"<human>: {user_input}\n<bot>:"
+    response = generate_response(prompt)
+    logging.debug(f"Generated response: {response}")
+    return jsonify({"response": response})
+
+if __name__ == "__main__":
+    # Get the port from environment variable or default to 5000
+    port = int(os.getenv("PORT", 5000))
+    logging.info(f"Starting Flask app on port {port}")
+    app.run(debug=True, host="0.0.0.0", port=port)

requirements.txt

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+flask
+flask-cors
+torch
+transformers