Update main.py

main.py

@@ -45,6 +45,33 @@ bucket = storage.bucket()
 
 # --- Gemini Client ---
 client = genai.Client(api_key=os.getenv("Gemini"))
+model_name = "gemini-2.0-flash-thinking-exp"
+
+
+class GenericEvaluator:
+    def generate_prompt(self, participant_info: dict) -> str:
+        prompt = f"""
+You are an expert evaluator for a small business incubator in South Africa, reviewing candidate applications. Use your own expertise, critical thinking, and judgment to assess the following applicant. There are no predefined criteria or weights — your evaluation should be holistic and based on the information provided.
+
+Participant Info:
+{json.dumps(participant_info, indent=2)}
+
+Based on your assessment, provide:
+1. "AI Recommendation": either "Accept" or "Reject"
+2. "AI Score": a score out of 100 reflecting overall business quality or readiness
+3. "Justification": a brief explanation for your decision
+4. "Recommended Interventions": A description of the interventions or what help the business should get from the incubator to take them to the next level"
+Return your output strictly as a JSON dictionary with these keys: "AI Recommendation", "AI Score" and "Justification", "Interventions".
+"""
+        return prompt
+
+    def parse_gemini_response(self, response_text: str) -> dict:
+        try:
+            return json.loads(response_text)
+        except Exception as e:
+            return {"error": str(e), "raw_response": response_text}
+
+
 
 # --- FAISS Setup ---
 INDEX_PATH = "vector.index"
@@ -427,6 +454,91 @@ def financial_statement():
 
     return jsonify({"report": report, "cached": False}), 200
 
+# AI Screening endpoint
+@app.route('/api/evaluate', methods=['POST'])
+def evaluate_participant():
+    try:
+        data = request.json
+        participant_id = data.get("participantId")
+        participant_info = data.get("participantInfo", {})
+
+        evaluator = GenericEvaluator()
+        prompt = evaluator.generate_prompt(participant_info)
+
+        response = client.models.generate_content(
+            model=model_name,
+            contents=prompt
+        )
+
+        evaluation = evaluator.parse_gemini_response(response.text)
+
+        return jsonify({
+            "status": "success",
+            "participantId": participant_id,
+            "evaluation": evaluation
+        })
+
+    except Exception as e:
+        return jsonify({
+            "status": "error",
+            "message": str(e)
+        }), 500
+
+
+@app.route('/api/batch-evaluate', methods=['POST'])
+def batch_evaluate():
+    try:
+        participants = request.json.get('participants', [])
+        results = []
+
+        evaluator = GenericEvaluator()
+
+        for item in participants:
+            participant_id = item.get("participantId")
+            participant_info = item.get("participantInfo", {})
+            prompt = evaluator.generate_prompt(participant_info)
+
+            response = client.models.generate_content(
+                model=model_name,
+                contents=prompt
+            )
+
+            evaluation = evaluator.parse_gemini_response(response.text)
+
+            results.append({
+                "participantId": participant_id,
+                "evaluation": evaluation
+            })
+
+        return jsonify({
+            "status": "success",
+            "evaluations": results
+        })
+
+    except Exception as e:
+        return jsonify({
+            "status": "error",
+            "message": str(e)
+        }), 500
+
+
+@app.route('/api/shortlist', methods=['GET'])
+def get_shortlist():
+    try:
+        # Placeholder logic
+        return jsonify({
+            "status": "success",
+            "shortlist": []
+        })
+    except Exception as e:
+        return jsonify({
+            "status": "error",
+            "message": str(e)
+        }), 500
+
+
+
+
 # --------- Run the App ---------
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860, debug=True)