Update app.py

app.py

@@ -24,49 +24,59 @@ 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 separately
+    # 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 (2048 - prompt tokens)
+    # Calculate remaining tokens for resume text
     max_resume_tokens = 2048 - prompt_tokens
     
-    # Tokenize and strictly truncate the resume text to fit within the remaining token space
+    # Tokenize and truncate resume text
     resume_tokenized = tokenizer(resume_text, truncation=True, max_length=max_resume_tokens, return_tensors="pt")
     
-    # Decode the truncated resume back into a string
+    # Decode the truncated resume text back into a string
     truncated_resume_text = tokenizer.decode(resume_tokenized['input_ids'][0], skip_special_tokens=True)
     
-    # Combine the prompt and the truncated resume text
+    # Combine prompt and truncated resume text
     final_prompt = f"{prompt_text}{truncated_resume_text}\n\nRoast:"
     
-    # Generate the roast
+    # Generate roast
     generator = pipeline('text-generation', model=model, tokenizer=tokenizer)
-    
-    # Ensure generated roast doesn't exceed token limit
     roast = generator(final_prompt, max_new_tokens=50, num_return_sequences=1)
     
     return roast[0]['generated_text']
 
-# Gradio interface function
-def roast_resume(file):
-    if file.name.endswith('.pdf'):
-        resume_text = extract_text_from_pdf(file)
-    elif file.name.endswith('.docx'):
-        resume_text = extract_text_from_docx(file)
+# 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 "Unsupported file format. Please upload a PDF or DOCX file."
+        return "No resume provided."
     
+    # Generate the roast based on extracted or pasted resume text
     roast = generate_roast(resume_text)
     return roast
 
-# Create Gradio interface
-interface = gr.Interface(fn=roast_resume, inputs="file", outputs="text", 
-                         title="Resume Roaster",
-                         description="Upload your resume in PDF or DOCX format, and let the AI roast it!")
+# 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 the app
+# Launch Gradio app
 interface.launch()