Create app.py

app.py

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+import pdfplumber
+import docx
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Function to extract text from PDF using pdfplumber
+def extract_text_from_pdf(pdf_file):
+    with pdfplumber.open(pdf_file) as pdf:
+        text = ''
+        for page in pdf.pages:
+            text += page.extract_text()
+    return text
+
+# Function to extract text from DOCX
+def extract_text_from_docx(docx_file):
+    doc = docx.Document(docx_file)
+    full_text = []
+    for paragraph in doc.paragraphs:
+        full_text.append(paragraph.text)
+    return '\n'.join(full_text)
+
+# Function to generate roast based on resume text
+def generate_roast(resume_text):
+    tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
+    model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")
+
+    # Define the prompt
+    prompt_text = "Roast this resume:\n\n"
+    
+    # Tokenize the prompt
+    prompt_tokenized = tokenizer(prompt_text, return_tensors="pt")
+    prompt_tokens = prompt_tokenized['input_ids'].shape[1]
+    
+    # Calculate remaining tokens for resume text
+    max_resume_tokens = 2048 - prompt_tokens
+    
+    # Tokenize and truncate resume text
+    resume_tokenized = tokenizer(resume_text, truncation=True, max_length=max_resume_tokens, return_tensors="pt")
+    
+    # Decode the truncated resume text back into a string
+    truncated_resume_text = tokenizer.decode(resume_tokenized['input_ids'][0], skip_special_tokens=True)
+    
+    # Combine prompt and truncated resume text
+    final_prompt = f"{prompt_text}{truncated_resume_text}\n\nRoast:"
+    
+    # Generate roast
+    generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
+    roast = generator(final_prompt, max_new_tokens=50, num_return_sequences=1)
+    
+    return roast[0]['generated_text']
+
+# Function to handle file uploads and extract text from resume files
+def roast_resume(file=None, resume_text=None):
+    if file:
+        # Handle file uploads for PDF or DOCX
+        if file.name.endswith('.pdf'):
+            resume_text = extract_text_from_pdf(file)
+        elif file.name.endswith('.docx'):
+            resume_text = extract_text_from_docx(file)
+        else:
+            return "Unsupported file format. Please upload a PDF or DOCX file."
+    elif resume_text:
+        # Use pasted resume text
+        pass
+    else:
+        return "No resume provided."
+    
+    # Generate the roast based on extracted or pasted resume text
+    roast = generate_roast(resume_text)
+    return roast
+
+# Gradio interface with file upload or text input options
+interface = gr.Interface(
+    fn=roast_resume, 
+    inputs=[gr.File(label="Upload Resume (PDF/DOCX)"), gr.Textbox(label="Or Paste Your Resume")], 
+    outputs="text", 
+    title="Resume Roaster",
+    description="Upload your resume in PDF/DOCX format or paste your resume text, and let the AI roast it!"
+)
+
+# Launch Gradio app
+interface.launch()