Upload 2 files

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+import os
+import re
+import json
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+adapter_path = os.path.join(script_dir, "lora-playwright-adapter")
+model_name = "mistralai/Mistral-7B-Instruct-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Ensure padding token
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+model = PeftModel.from_pretrained(base_model, adapter_path)
+model.eval()
+
+# Example test goals (you can extend this to load from Excel/CSV)
+
+def generate_action_sequence(test_goals):
+    full_response = []
+    for goal in test_goals:  
+       
+        prompt = f"Goal: {goal}\nReturn only one valid JSON array, no explanation.\nOutput:"
+
+        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to(model.device)
+
+        with torch.no_grad():
+            outputs = model.generate(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            max_new_tokens=150,
+            pad_token_id=tokenizer.pad_token_id,
+            top_p=1.0,
+            repetition_penalty=1.2,
+            do_sample=False  
+            )
+
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        # Extract JSON array part
+        match = re.search(r'\[.*\]', response, re.DOTALL)
+        if match:
+            response_text = match.group(0)
+            try:
+                response_json = json.loads(response_text)
+                full_response.extend(response_json) 
+            except json.JSONDecodeError:
+                print(f"Invalid JSON for goal: {goal}")
+        else:
+            print(f"No JSON found for goal: {goal}")
+ 
+    return full_response
+
+@app.route("/")
+def health():
+    return "OK", 200
+
+@app.route("/generate", methods=["POST"])
+def generate():
+    data = request.get_json()
+    test_goals = data.get("goals", [])
+    result = generate_action_sequence(test_goals)
+    return jsonify({"result": result})
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 5000))
+    app.run(host="0.0.0.0", port=port)
+ 

requirements.txt

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+openai
+langchain
+chromadb
+python-dotenv
+rich
+typer