Create app.py

app.py

@@ -0,0 +1,83 @@
+import gradio as gr
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
+
+model_name = "nouamanetazi/cover-letter-t5-base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+
+
+def generate_cover_letter(
+    name, job, company, background, experiences, max_length=300, temperature=1.0, top_p=0.9, max_time=10
+):
+    model_args = {
+        "max_length": max_length,
+        "temperature": temperature,
+        "top_p": top_p,
+        # "top_k": 120,
+        "early_stopping": True,
+        "max_time": max_time,
+        "do_sample": True,  # do_sample=False to force deterministic output
+        "num_return_sequences": 1,  # number of samples to return
+        "min_length": 100,
+        "num_beams": 4,
+        # "num_beam_groups": 1,
+        # "diversity_penalty": 0,
+        # "repetition_penalty": 5.0,
+        # "length_penalty": 0,
+        # "remove_invalid_values": True,
+        "no_repeat_ngram_size": 3,
+    }
+    # Load the tokenizer and the distilgpt2 model
+    # Set up the transformers pipeline
+    text_generator = pipeline(
+        "text2text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1
+    )
+    # Generate the text
+    prompt = f"coverletter name: {name} job: {job} at {company} background: {background} experiences: {experiences}"
+    generated_text = text_generator(prompt, **model_args)[0]["generated_text"]
+    return generated_text
+
+
+title = "A Cover Letter Generator"
+description = ""
+article = '<div style="text-align:center">This is a Space App for the project <a href="https://discuss.huggingface.co/t/build-a-cover-letter-generator/11721">build-a-cover-letter-generator</a> from the HuggingFace course 2 🤗 featuring a text generation model in english, based on the model t5-base.</div>'
+examples = None
+interface = gr.Interface(
+    fn=generate_cover_letter,
+    inputs=[
+        gr.inputs.Textbox(
+            label="Your name",
+            default="Sakil Ansari",
+        ),
+        gr.inputs.Textbox(
+            label="The job you want to apply for",
+            default="Data Scientist",
+        ),
+        gr.inputs.Textbox(
+            label="The company you want to apply for",
+            default="Google",
+        ),
+        gr.inputs.Textbox(
+            lines=2,
+            label="Your education/background",
+            default="Master of Technology in Machine learning",
+        ),
+        gr.inputs.Textbox(
+            lines=3,
+            label="Your skills/previous experiences",
+            default="I am the Author of Book and MTech in Machine Learning and achievement-driven professional with an experience of 3+ years in Data Science/Machine Learning/NLP/ Deep Learning/Data analytics. I am highly skilled in libraries like Sklearn, Numpy, Pandas, Matplotlib, Seaborn, Tensorflow, Faster-RCNN, Keras, Pytorch, FastAI, PowerBI/Tableau for Data Visualization, SQL/Oracle/NoSQL for databases and experience in NLP use cases related to named entity recognition, text summarization, text similarity, text generation.",
+        ),
+        gr.inputs.Slider(20, 2048, default=400, label="Max Length"),
+        gr.inputs.Slider(0, 3, default=1.2, label="Temperature"),
+        gr.inputs.Slider(0, 1, default=0.9, label="Top P"),
+        gr.inputs.Slider(1, 200, default=20, label="Max time"),
+    ],
+    outputs=[gr.outputs.Textbox(type="str", label="Cover Letter")],
+    title=title,
+    description=description,
+    examples=examples,
+    article=article,
+    layout="horizontal",
+)
+interface.launch(inline=False, debug=False)