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Dockerfile

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+# Use lightweight Python
+FROM python:3.11-slim
+
+# Set working directory
+WORKDIR /app
+
+# Copy requirements first (better caching)
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy all files
+COPY . .
+
+# Expose port (HF uses 7860 internally)
+EXPOSE 7860
+
+# Run OpenEnv app
+CMD ["python", "-m", "openenv"]

README.md

@@ -1,10 +1,178 @@
----
-title: Test Work
-emoji: 🦀
-colorFrom: gray
-colorTo: gray
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+#  Smart Career Advisor Agent (OpenEnv)
+
+A lightweight **OpenEnv-compatible smart agent** that provides career guidance based on user interests, with intelligent suggestions and random exploration tips.
+
+---
+
+##  Features
+
+*  Intent-based career suggestions (AI, Web, Data, Design)
+*  Smart agent behavior with structured responses
+*  Random growth tips for exploration
+*  Stateless design (OpenEnv compliant)
+*  Ready for Hugging Face Spaces deployment
+
+---
+
+##  Project Structure
+
+```
+smart_agent_openenv/
+│
+├── inference.py        # OpenEnv entrypoint (REQUIRED)
+├── app.py              # Application controller
+├── agents.py           # Smart agent logic
+├── requirements.txt    # Dependencies
+├── openenv.yaml        # OpenEnv config (optional)
+├── Dockerfile          # HF deployment
+└── README.md
+```
+
+---
+
+##  Requirements
+
+* Python **3.10 / 3.11 / 3.12**
+* pip
+* Git
+
+---
+
+##  Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+##  Local Testing
+
+Since CLI may not work on all systems, test manually:
+
+```bash
+python test_openenv.py
+```
+
+Or create a quick test:
+
+```python
+from inference import predict, reset
+
+print(predict({"query": "I like AI"}))
+print(reset())
+```
+
+---
+
+##  OpenEnv Contract
+
+This project follows required OpenEnv structure:
+
+*  `inference.py` at root
+*  `predict(input_data)` function
+*  `reset()` function
+*  Stateless execution
+
+---
+
+##  Docker Deployment (Hugging Face)
+
+Build runs automatically on HF Spaces using Dockerfile.
+
+### Steps:
+
+1. Push code to GitHub
+2. Go to Hugging Face Spaces
+3. Create new Space:
+
+   * SDK → **Docker**
+4. Connect repo or upload files
+5. Wait for build 
+
+---
+
+##  Usage Example
+
+Input:
+
+```json
+{
+  "query": "I am interested in AI"
+}
+```
+
+Output:
+
+```json
+{
+  "output": {
+    "intent": "ai",
+    "careers": ["AI Engineer", "ML Engineer", "Deep Learning Engineer"],
+    "tip": "Build real-world projects",
+    "message": "Based on your interest in ai, these careers fit you."
+  }
+}
+```
+
+---
+
+##  Common Issues & Fixes
+
+###  `openenv not recognized`
+
+Use:
+
+```bash
+python -m pip install openenv-core
+```
+
+---
+
+###  `reset post failed`
+
+✔ Ensure `reset()` exists in `inference.py`
+
+---
+
+###  `inference.py not detected`
+
+✔ File must be in root directory
+
+---
+
+###  Python version issues
+
+✔ Use Python 3.10–3.12 (avoid 3.13)
+
+---
+
+##  Submission Steps
+
+1. Build project using OpenEnv structure
+2. Test locally
+3. Push to GitHub
+4. Deploy on Hugging Face Spaces
+5. Submit Space URL
+
+---
+
+##  Future Improvements
+
+* Multi-agent system (planner + executor)
+* Memory-based reasoning (vector / graph)
+* Integration with LLM APIs
+* Real-world problem solving agents
+
+---
+
+##  Author
+
+**Junaid Khan**
+AI Researcher & Developer
+
+---
+
+##  License
+
+Open-source project for educational and experimental use.

agents.py

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+import random
+
+class SmartCareerAgent:
+    def __init__(self):
+        self.career_map = {
+            "ai": ["AI Engineer", "ML Engineer", "Deep Learning Engineer"],
+            "web": ["Frontend Developer", "Backend Developer", "Full Stack Developer"],
+            "data": ["Data Analyst", "Data Scientist", "Data Engineer"],
+            "design": ["UI/UX Designer", "Product Designer"]
+        }
+
+        self.tips = [
+            "Build real-world projects",
+            "Create a strong GitHub portfolio",
+            "Participate in hackathons",
+            "Learn problem-solving (DSA)",
+            "Follow industry trends"
+        ]
+
+    def detect_intent(self, query):
+        query = query.lower()
+        for key in self.career_map:
+            if key in query:
+                return key
+        return "unknown"
+
+    def respond(self, query):
+        intent = self.detect_intent(query)
+
+        if intent != "unknown":
+            return {
+                "intent": intent,
+                "careers": self.career_map[intent],
+                "tip": random.choice(self.tips),
+                "message": f"Based on your interest in {intent}, these careers fit you."
+            }
+
+        return {
+            "intent": "unknown",
+            "careers": [],
+            "tip": random.choice(self.tips),
+            "message": "Please tell me your interest (AI, Web, Data, Design)."
+        }

app.py

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+from agents import SmartCareerAgent
+
+class SmartAgentApp:
+    def __init__(self):
+        self.agent = SmartCareerAgent()
+
+    def run(self, query):
+        return self.agent.respond(query)

inference.py

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+from app import SmartAgentApp
+
+app_instance = SmartAgentApp()
+
+def predict(input_data):
+    query = input_data.get("query", "")
+    
+    result = app_instance.run(query)
+    
+    return {
+        "output": result
+    }
+
+
+def reset():
+    global app_instance
+    app_instance = SmartAgentApp()
+    
+    return {"status": "reset successful"}

openenv.yaml

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+entrypoint: inference.py

requirements.txt

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+openenv-core

test_openenv.py

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+from inference import predict, reset
+
+# Test predict
+print("Testing predict...")
+output = predict({"query": "I like AI"})
+print(output)
+
+# Test reset
+print("Testing reset...")
+print(reset())