Update app.py

app.py

@@ -1,25 +1,60 @@
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import os
 import gradio as gr
+import PyPDF2
+import docx2txt
 
-# Get Hugging Face token from environment variable (safe in secrets)
+# Load token and model
 hf_token = os.environ.get("hf_space_token")
-
 model_name = "google/gemma-3-1b-it"
 tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
 model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)
 
-def generate_response(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt")
-    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+# Resume text extraction
+def extract_text_from_resume(file):
+    if file is None:
+        return "No file uploaded."
+    ext = file.name.split('.')[-1].lower()
+    if ext == "pdf":
+        reader = PyPDF2.PdfReader(file)
+        return "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
+    elif ext == "docx":
+        return docx2txt.process(file.name)
+    else:
+        return "Unsupported file format."
+
+# Function to generate LLM response
+def analyze_resume(job_description, resume_file, analyze_with_jd):
+    resume_text = extract_text_from_resume(resume_file)
+    if not resume_text.strip():
+        return "Unable to extract resume content."
+
+    if analyze_with_jd and job_description.strip():
+        prompt = (
+            f"Compare the following resume with this job description:\n\n"
+            f"Job Description:\n{job_description}\n\n"
+            f"Resume:\n{resume_text}\n\n"
+            f"Give a short summary of how well this resume matches the job."
+        )
+    else:
+        prompt = f"Summarize the following resume:\n\n{resume_text}"
+
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+    outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-iface = gr.Interface(
-    fn=generate_response,
-    inputs=gr.Textbox(lines=2, placeholder="Ask something..."),
-    outputs="text",
-    title="Chat with Gemma",
-    description="Chatbot powered by Google's Gemma model"
-)
+# Build Gradio UI
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            analyze_checkbox = gr.Checkbox(label="Analyze with Job Description", value=True)
+            job_desc = gr.Textbox(label="Job Description", lines=8, placeholder="Paste job description here...")
+            resume_file = gr.File(label="Upload Resume (PDF or DOCX)", file_types=[".pdf", ".docx"])
+        with gr.Column():
+            parsed_output = gr.Textbox(label="Gemma Analysis Output", lines=20)
+
+    resume_file.change(fn=analyze_resume, inputs=[job_desc, resume_file, analyze_checkbox], outputs=parsed_output)
+    job_desc.change(fn=analyze_resume, inputs=[job_desc, resume_file, analyze_checkbox], outputs=parsed_output)
+
+demo.launch()
 
-iface.launch()