Update use_case_generation.py

use_case_generation.py

@@ -1,22 +1,36 @@
-from transformers import pipeline
+import openai
+import os
 
-# Initialize NLP model for text generation
-generator = pipeline("text-generation", model="gpt-2")
+# Set up your OpenAI API key
+openai.api_key = os.getenv("22ec84421ec24230a3638d1b51e3a7dc") 
 
 def generate_use_cases(company_info):
     use_cases = []
     references = ["McKinsey AI Report", "Deloitte Industry Insights"]
-    
+
+    # Generate suggestions based on company focus areas
     if "customer experience" in company_info["focus_areas"]:
-        suggestion = generator("Create a GenAI-driven chatbot for enhanced customer support.", max_length=50)[0]['generated_text']
+        suggestion = get_openai_response("Suggest a GenAI-driven chatbot for enhanced customer support.")
         use_cases.append(suggestion)
     
     if "operations" in company_info["focus_areas"]:
-        suggestion = generator("Utilize predictive maintenance models to streamline operations.", max_length=50)[0]['generated_text']
+        suggestion = get_openai_response("Propose using predictive maintenance models to streamline operations.")
         use_cases.append(suggestion)
     
     if "supply chain" in company_info["focus_areas"]:
-        suggestion = generator("Implement real-time analytics for supply chain transparency.", max_length=50)[0]['generated_text']
+        suggestion = get_openai_response("Describe how real-time analytics could improve supply chain transparency.")
         use_cases.append(suggestion)
     
     return {"use_cases": use_cases, "references": references}
+
+def get_openai_response(prompt, model="gpt-4"):
+    try:
+        response = openai.ChatCompletion.create(
+            model=model,
+            messages=[{"role": "user", "content": prompt}]
+        )
+        return response['choices'][0]['message']['content']
+    except Exception as e:
+        print(f"Error generating response from OpenAI: {e}")
+        return "Could not generate use case."
+