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app.py

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+import gradio as gr
+import requests
+import json
+import PyPDF2
+from io import BytesIO
+import os
+
+# Global variable to store extracted resume text
+current_resume_text = ""
+
+DEEPSEEK_API_KEY = os.environ.get("DEEPSEEK_API_KEY")
+
+
+
+def extract_text_from_pdf(pdf_file):
+    """Extract text from uploaded PDF file"""
+    try:
+        pdf_reader = PyPDF2.PdfReader(pdf_file)
+        text = ""
+        for page in pdf_reader.pages:
+            text += page.extract_text() + "\n"
+        return text.strip()
+    except Exception as e:
+        raise Exception(f"Error reading PDF: {str(e)}")
+
+
+def process_resume(pdf_file):
+    """Process the uploaded resume and store text globally"""
+    global current_resume_text
+
+    if not pdf_file:
+        return "Please upload a PDF file.", ""
+
+    try:
+        current_resume_text = extract_text_from_pdf(pdf_file)
+        if not current_resume_text:
+            return "No text could be extracted from the PDF. Please ensure the PDF contains readable text.", ""
+
+        success_message = f"✅ Resume processed successfully! ({len(current_resume_text)} characters extracted)\n\nYou can now ask questions about this resume in the chat."
+        return success_message, ""
+
+    except Exception as e:
+        current_resume_text = ""
+        return f"Error processing resume: {str(e)}", ""
+
+
+def answer_question(question, chat_history):
+    """Answer questions about the uploaded resume"""
+    global current_resume_text
+
+    if not current_resume_text:
+        response = "❌ Please upload and process a resume first before asking questions."
+        chat_history.append([question, response])
+        return chat_history, ""
+
+    if not question.strip():
+        response = "Please enter a question about the resume."
+        chat_history.append([question, response])
+        return chat_history, ""
+
+    try:
+        prompt = f"""
+        Based on the following resume content, please answer the user's question accurately and concisely:
+        Resume Content:
+        {current_resume_text}
+        User Question: {question}
+        Please provide a clear, specific answer based only on the information available in the resume. If the information is not available in the resume, please state that clearly.
+        """
+
+        response = requests.post(
+            url="https://openrouter.ai/api/v1/chat/completions",
+            headers={
+                "Authorization": f"Bearer {DEEPSEEK_API_KEY}",
+                "Content-Type": "application/json",
+            },
+            data=json.dumps({
+                "model": "deepseek/deepseek-chat",
+                "messages": [
+                    {
+                        "role": "system",
+                        "content": "You are a helpful assistant that answers questions about resumes. Base your answers strictly on the resume content provided. Be concise but thorough."
+                    },
+                    {
+                        "role": "user",
+                        "content": prompt
+                    }
+                ]
+            })
+        )
+
+        if response.status_code == 200:
+            result = response.json()
+            answer = result['choices'][0]['message']['content']
+        else:
+            answer = f"❌ API Error: {response.text}"
+
+        chat_history.append([question, answer])
+        return chat_history, ""
+
+    except Exception as e:
+        response = f"❌ Error: {str(e)}"
+        chat_history.append([question, response])
+        return chat_history, ""
+
+
+def clear_chat():
+    return []
+
+
+def get_sample_questions():
+    return [
+        "What are the key technical skills mentioned?",
+        "What is their educational background?",
+        "What certifications do they have?",
+        "Rate this resume on a scale of 1-10"
+    ]
+
+# Gradio UI
+def create_ui():
+    with gr.Blocks(theme=gr.themes.Soft(primary_hue="green", secondary_hue="green")) as demo:
+        gr.Markdown("<h1 style='text-align: center; color: green;'>ResumAI</h1>")
+        gr.Markdown("<p style='text-align: center; color: white;'>Upload a resume (PDF) and ask specific questions about the candidate's skills, experience, and qualifications.</p>")
+
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("### 📄 Upload Resume")
+                pdf_input = gr.File(label="Upload PDF Resume", file_types=[".pdf"])
+                process_button = gr.Button("Process Resume", variant="primary")
+                status_text = gr.Textbox(label="Status", lines=3, interactive=False)
+
+                gr.Markdown("### 💡 Sample Questions")
+                sample_questions = get_sample_questions()
+                for question in sample_questions:
+                    gr.Markdown(f"• {question}")
+
+            with gr.Column(scale=2):
+                gr.Markdown("### 💬 Ask Questions About the Resume")
+                chatbot = gr.Chatbot(label="Q&A Chat", height=570)
+                with gr.Row():
+                    question_input = gr.Textbox(label="Your Question", placeholder="Ask anything about the resume...", scale=4)
+                    ask_button = gr.Button("Ask", variant="primary", scale=1)
+                with gr.Row():
+                    clear_button = gr.Button("Clear Chat", variant="secondary")
+
+        process_button.click(fn=process_resume, inputs=[pdf_input], outputs=[status_text, question_input])
+        ask_button.click(fn=answer_question, inputs=[question_input, chatbot], outputs=[chatbot, question_input])
+        question_input.submit(fn=answer_question, inputs=[question_input, chatbot], outputs=[chatbot, question_input])
+        clear_button.click(fn=clear_chat, outputs=[chatbot])
+
+    return demo
+
+if __name__ == "__main__":
+    print("Starting Resume Q&A Assistant with DeepSeek...")
+    demo = create_ui()
+    demo.launch()

requirements.txt

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+gradio
+requests
+PyPDF2