Create app.py

app.py

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+import fitz
+import joblib
+import numpy as np
+import gradio as gr
+from langchain.tools import Tool
+from langchain.chat_models import ChatOpenAI
+from langchain.agents import initialize_agent, AgentType
+import openai
+
+
+openai.api_key=os.getenv("OPENAI_API_KEY")
+
+# STEP 2: Load model and vectorizer
+model = joblib.load("xgb_resume_model.pkl")
+vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+# Hybrid thresholds
+q_low = 0.5166
+q_high = 2.8319
+
+# Weighted keywords
+weighted_keywords = {
+    'llm': 3.5, 'langchain': 3.5, 'openai': 3, 'data analysis': 2,
+    'sql': 2, 'teaching': 3, 'crm': 3, 'project management': 3.5
+}
+
+# Resume text extraction
+def extract_resume_text(file):
+    doc = fitz.open(file.name)
+    return " ".join([page.get_text() for page in doc]).strip()
+
+# Resume strength
+def predict_strength(resume_text):
+    X = vectorizer.transform([resume_text])
+    prediction = model.predict(X)[0]
+    score = sum(weight for kw, weight in weighted_keywords.items() if kw in resume_text.lower())
+    norm_score = score / np.log(len(resume_text.split()) + 1)
+    if prediction == 'Average' and norm_score >= q_high:
+        prediction = 'Strong'
+    elif prediction == 'Average' and norm_score < q_low:
+        prediction = 'Weak'
+    return f"✅ Resume Strength: {prediction}"
+
+# Job role
+def predict_role(resume_text):
+    roles = ["AI Engineer", "Data Scientist", "Project Manager", "Teacher", "Sales Executive"]
+    prompt = f"""
+You are a job role classification expert. Pick one best-fit role from the list: {', '.join(roles)}.
+Resume:
+{resume_text}
+Only respond with a single job role.
+"""
+    response = ChatOpenAI(model="gpt-4o", openai_api_key).invoke(prompt)
+    return f"🧩 Predicted Role: {response.content.strip()}"
+
+# Feedback logic
+def gpt_resume_feedback(resume_text):
+    prompt = f"""
+You are an expert resume reviewer. Provide structured feedback.
+
+Resume:
+{resume_text}
+"""
+    response = openai.ChatCompletion.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.3
+    )
+    return response.choices[0].message.content.strip()
+
+# STEP 3: Tools
+strength_tool = Tool.from_function(predict_strength, name="Strength Tool", description="ML resume strength")
+role_tool = Tool.from_function(predict_role, name="Role Tool", description="GPT role classifier")
+feedback_tool = Tool.from_function(gpt_resume_feedback, name="Feedback Tool", description="GPT resume feedback")
+
+llm = ChatOpenAI(model="gpt-4o", openai_api_key))
+agent_executor = initialize_agent(
+    tools=[strength_tool, role_tool, feedback_tool],
+    llm=llm,
+    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+    verbose=True
+)
+
+# ✅ STEP 4: Main routing logic (with Career Guidance Tool)
+def gpt_career_guidance(resume_text="", question=""):
+    
+
+    if resume_text:
+        prompt = f"""
+You are a friendly AI career mentor. Based on the resume below, answer the user's question politely and clearly.
+Use the resume to personalize your advice.
+
+Resume:
+{resume_text}
+
+User Question:
+{question}
+"""
+    else:
+        prompt = f"""
+You are a helpful AI career mentor. The user didn't upload a resume.
+Provide a clear, friendly, and helpful response to this general career question:
+
+User Question:
+{question}
+"""
+    try:
+        response = openai.ChatCompletion.create(
+            model="gpt-4o",
+            messages=[{"role": "user", "content": prompt}],
+            temperature=0.5
+        )
+        return response.choices[0].message.content.strip()
+    except Exception as e:
+        return f"❌ Error in Career Guidance Agent: {str(e)}"
+
+# ✅ Final decision logic
+def agent_decision(resume_file=None, question=""):
+    resume_text = ""
+    if resume_file:
+        resume_text = extract_resume_text(resume_file)
+
+    q_lower = question.lower()
+
+    if resume_text and not question.strip():
+        return agent_executor.run(f"Analyze the resume and give both strength and role. Text: {resume_text}")
+
+    elif resume_text and "strength" in q_lower:
+        return strength_tool.run(resume_text)
+
+    elif resume_text and "role" in q_lower:
+        return role_tool.run(resume_text)
+
+    elif resume_text and "feedback" in q_lower:
+        return feedback_tool.run(resume_text)
+
+    elif question.strip():  # Career question with or without resume
+        return gpt_career_guidance(resume_text, question)
+
+    else:
+        return "⚠️ Please upload a resume or ask a question."
+
+# ✅ Clear button logic
+def clear_fields():
+    return None, "", ""
+
+with gr.Blocks(title="PathForge Agent App 🧠") as demo:
+    # ✅ Add Title (so it's visible like in your first screenshot)
+    gr.Markdown("<h1 style='text-align: center;'>PathForge Agent App 🧠</h1>")
+    gr.Markdown("<p style='text-align: center;'>Upload your resume or ask a question. This smart agent will decide which tool to use!</p>")
+
+    # ✅ How to Use Accordion
+    with gr.Accordion("🛠️ How to Use This App", open=False):
+        gr.Markdown("""
+**🔍 Use this app in 3 simple ways:**
+
+1. **Upload your resume** to get:
+   - Resume strength (Weak / Average / Strong)
+   - Suitable job role prediction
+
+2. **Ask a question** (optional), such as:
+   - "What’s my resume strength?"
+   - "Can you give resume feedback?"
+   - "What role suits my profile?"
+   - "How to grow my career in AI?"
+
+3. **Use both together** to get personalized guidance.
+
+If you only ask a general career question without a resume, the app will still respond with advice!
+""")
+
+    # ✅ Input Section
+    with gr.Row():
+        resume = gr.File(label="📄 Upload Resume", type="filepath", file_types=[".pdf"])
+        question = gr.Textbox(
+            label="💬 Ask something (optional)",
+            placeholder="Ask about resume, role, feedback, or career growth...",
+            lines=3
+        )
+
+    # ✅ Submit and Clear side-by-side
+    with gr.Row():
+        submit = gr.Button("🚀 Submit")
+        clear = gr.Button("🧹 Clear", variant="secondary")
+
+    output = gr.Textbox(label="📤 Response", lines=12)
+
+    # Button logic
+    submit.click(fn=agent_decision, inputs=[resume, question], outputs=output)
+    clear.click(fn=lambda: (None, "", ""), inputs=[], outputs=[resume, question, output])
+
+
+demo.launch()
+
+if __name__ == "__main__":
+    demo.launch()