copied project files into the PR

week3/muawiya/app/app.py

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+import os
+import requests
+from IPython.display import Markdown, display, update_display
+from openai import OpenAI
+from google.colab import drive
+from huggingface_hub import login
+from google.colab import userdata
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig, pipeline, TextGenerationPipeline
+import torch
+from consts import FALCON, MISTRAL, Databricks
+from dotenv import load_dotenv
+import json
+import ast
+import gradio as gr
+import re
+
+# Sign in to HuggingFace Hub
+load_dotenv()
+hf_token = os.getenv("HF_TOKEN")
+
+
+# Main Prompt
+prompt = """
+Generate one fake job posting for a {{role}}.
+
+Return only a single JSON object with:
+- title
+- description (5-10 sentences)
+- requirements (array of 4-6 strings)
+- location
+- company_name
+
+No explanations, no extra text.
+Only the JSON object.
+"""
+
+# Main Conf
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_quant_type="nf4"
+)
+
+def load_model_and_tokenizer():
+  tokenizer = AutoTokenizer.from_pretrained(MISTRAL, trust_remote_code=True)
+
+  model = AutoModelForCausalLM.from_pretrained(
+  MISTRAL,
+  device_map={"": "cuda"},
+  trust_remote_code=True,
+  offload_folder="/tmp/dolly_offload",
+  quantization_config=bnb_config
+  )
+
+  return model, tokenizer
+
+
+def generate_job(role="Software Engineer", model=None, tokenizer=None):
+    # prompt = prompt.format(role=role, n=n)
+    # outputs = generator(prompt, max_new_tokens=500, do_sample=True, temperature=0.9)
+    # return outputs[0]['generated_text']
+    
+    # Apply chat template formatting
+    # inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+    inputs = tokenizer(prompt.format(role=role), return_tensors="pt")
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+
+    # Generate output
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=600,
+        do_sample=True,
+        temperature=0.2,
+        top_p=0.9,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    # Decode and return
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return result
+
+def generate_jobs(role="Software Engineer", n=5):
+  model, tokenizer = load_model_and_tokenizer()
+  role = "Software Engineer"
+  fake_jobs = []
+  for i in range(n):
+      fake_jobs.append(generate_job(role=role, model=model, tokenizer=tokenizer))
+  return fake_jobs
+
+def extract_json_objects_from_text_block(texts):
+    """
+    Accepts either a single string or a list of strings.
+    Extracts all valid JSON objects from messy text blocks.
+    """
+    if isinstance(texts, str):
+        texts = [texts]  # wrap in list if single string
+
+    pattern = r"\{[\s\S]*?\}"
+    results = []
+
+    for raw_text in texts:
+        matches = re.findall(pattern, raw_text)
+        for match in matches:
+            try:
+                obj = json.loads(match)
+                results.append(obj)
+            except json.JSONDecodeError:
+                continue
+
+    return results
+
+def generate_ui(role, n):
+    try:
+        raw_jobs = generate_jobs(role, n)
+        parsed_jobs = extract_json_objects_from_text_block(raw_jobs)
+
+        if not isinstance(parsed_jobs, list) or not all(isinstance(item, dict) for item in parsed_jobs):
+            print("[ERROR] Parsed result is not a list of dicts")
+            return gr.update(value=[], visible=True), None
+
+        filename = f"data/{role.replace(' ', '_').lower()}_jobs.json"
+        with open(filename, "w") as f:
+            json.dump(parsed_jobs, f, indent=2)
+
+        print(f"[INFO] Returning {len(parsed_jobs)} jobs -> {filename}")
+        return parsed_jobs, filename
+
+    except Exception as e:
+        print(f"[FATAL ERROR] {e}")
+        return gr.update(value=[], visible=True), None
+
+
+if __name__ == "__main__":
+  with gr.Blocks() as demo:
+      gr.Markdown("# 🧠 Synthetic Job Dataset Generator")
+      gr.Markdown("Generate a structured dataset of job postings for a specific role.")
+
+      with gr.Row():
+          role_input = gr.Textbox(label="Job Role", placeholder="e.g. Software Engineer", value="Software Engineer")
+          n_input = gr.Number(label="Number of Samples", value=5, precision=0)
+
+      generate_button = gr.Button("🚀 Generate")
+      output_table = gr.JSON(label="Generated Dataset")
+      download_button = gr.File(label="Download JSON")
+
+      generate_button.click(
+          generate_ui,
+          inputs=[role_input, n_input],
+          outputs=[output_table, download_button]
+      )
+
+  demo.launch(debug=True, share=True)
+
+

week3/muawiya/app/consts.py

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+# Models 
+GPT = 'gpt2'
+FALCON = "tiiuae/falcon-rw-1b"
+MISTRAL = "mistralai/Mistral-7B-Instruct-v0.1"
+Databricks = "databricks/dolly-v2-3b"

week3/muawiya/app/requirements.txt

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+huggingface_hub==0.30.2
+ipython==8.12.3
+openai==1.76.2
+protobuf==6.30.2
+Requests==2.32.3
+torch==2.6.0+cu124
+transformers==4.51.3