Create app.py

app.py

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+import gradio as gr
+import openai
+import os
+from PyPDF2 import PdfReader
+import docx2txt
+
+# Load your API Key securely
+openai.api_key = os.environ.get("OPENAI_API_KEY")
+
+# Helper function to read PDF
+def read_pdf(file_obj):
+    reader = PdfReader(file_obj)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text() + "\n"
+    return text
+
+# Helper function to read DOCX
+def read_docx(file_obj):
+    return docx2txt.process(file_obj)
+
+# Main SmartHire Agent function
+def analyze_resumes(resume_files, job_description, min_years_exp=0):
+    if not resume_files or not job_description:
+        return "⚠️ Please upload resumes and provide a job description."
+
+    resumes = []
+    for file in resume_files:
+        if file.name.endswith('.pdf'):
+            text = read_pdf(file)
+        elif file.name.endswith('.docx'):
+            text = read_docx(file)
+        else:
+            text = ""
+        resumes.append({
+            "filename": file.name,
+            "content": text
+        })
+
+    # SmartHire System Prompt
+    system_prompt = f"""
+You are SmartHire, an AI Job Screening Assistant.
+
+Your tasks:
+- Analyze all candidate resumes.
+- Compare against the Job Description.
+- Rank candidates from best to worst fit.
+- For each candidate, summarize:
+    - Main Strengths
+    - Potential Risks
+    - Estimated Years of Experience (guess if not explicitly stated)
+- Only select candidates who meet or exceed {min_years_exp} years of experience.
+
+Respond in a structured JSON format:
+[
+  {{
+    "name": "Candidate Name (or filename)",
+    "strengths": ["strength1", "strength2"],
+    "risks": ["risk1", "risk2"],
+    "score": number (higher is better)
+  }},
+  ...
+]
+"""
+
+    # Create the user content
+    user_content = f"Job Description:\n{job_description}\n\nResumes:\n"
+    for idx, resume in enumerate(resumes):
+        user_content += f"\n---\nResume {idx+1} ({resume['filename']}):\n{resume['content']}\n"
+
+    # Call GPT-4 Turbo
+    response = openai.ChatCompletion.create(
+        model="gpt-4-turbo",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_content}
+        ],
+        temperature=0.2,
+        max_tokens=4096
+    )
+
+    # Extract the output JSON
+    import json
+    try:
+        candidates = json.loads(response["choices"][0]["message"]["content"])
+    except Exception as e:
+        return f"⚠️ Failed to parse AI response. Error: {e}"
+
+    # Sort by score
+    candidates = sorted(candidates, key=lambda x: x["score"], reverse=True)
+
+    # Create Gradio Cards
+    cards = []
+    for idx, candidate in enumerate(candidates[:3]):  # Only Top 3
+        strengths = "\n".join([f"- ✅ {s}" for s in candidate["strengths"]])
+        risks = "\n".join([f"- ⚠️ {r}" for r in candidate["risks"]])
+        card = f"""
+### {idx+1}. {candidate['name']}
+
+**Strengths:**
+{strengths}
+
+**Risks:**
+{risks}
+
+**Fit Score:** {candidate['score']} ⭐
+"""
+        cards.append(card)
+
+    return cards
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("# 🧠 SmartHire Pro — AI Job Screening Assistant")
+    gr.Markdown("Upload resumes and input the Job Description to rank and analyze candidates with AI.")
+
+    with gr.Row():
+        resumes = gr.File(label="Upload Resumes (.pdf, .docx)", file_types=[".pdf", ".docx"], file_count="multiple")
+        jd = gr.Textbox(lines=8, label="Paste Job Description")
+        min_exp = gr.Number(label="Minimum Years of Experience (Optional)", value=0)
+
+    submit = gr.Button("Analyze Candidates")
+    with gr.Column():
+        output1 = gr.Markdown()
+        output2 = gr.Markdown()
+        output3 = gr.Markdown()
+
+    submit.click(analyze_resumes, inputs=[resumes, jd, min_exp], outputs=[output1, output2, output3])
+
+# Launch
+if __name__ == "__main__":
+    demo.launch()