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app.py

@@ -15,18 +15,100 @@ simulation_manager = SimulationManager()
 REMOTE_BACKEND = "https://auxteam-tiny-factory.hf.space"
 
 def generate_personas(business_description, customer_profile, num_personas, api_key=None):
-    if api_key: os.environ["BLABLADOR_API_KEY"] = api_key
-    use_remote = random.random() < 0.5
-    if use_remote:
-        try:
-            response = requests.post(f"{REMOTE_BACKEND}/api/generate_personas", json={"data": [business_description, customer_profile, num_personas, ""]}, timeout=120)
-            if response.status_code == 200: return response.json()["data"][0]
-        except: pass
-    from tinytroupe.factory.tiny_person_factory import TinyPersonFactory
-    factory = TinyPersonFactory(context=f"{business_description} {customer_profile}", total_population_size=int(num_personas))
-    personas = factory.generate_people(number_of_people=int(num_personas))
-    return [p._persona for p in personas]
+    if api_key:
+        os.environ["BLABLADOR_API_KEY"] = api_key
+        os.environ["OPENAI_API_KEY"] = api_key
+    
+    import json
+    from gradio_client import Client
+    import openai
+    
+    client = openai.OpenAI()
+    dp_client = Client("THzva/deeppersona-experience")
+    
+    personas = []
+    
+    for _ in range(int(num_personas)):
+        # 1. Generate initial parameters for the 200 API call
+        prompt_1 = f"""
+Given the following business description and customer profile:
+Business: {business_description}
+Customer: {customer_profile}
+
+Generate realistic parameters for a persona that fits this profile. Return ONLY a valid JSON object with these EXACT keys:
+"Age" (number), "Gender" (string), "Occupation" (string), "City" (string), "Country" (string), "Personal Values" (string), "Life Attitude" (string), "Life Story" (string), "Interests and Hobbies" (string).
+Keep the string fields concise (1-2 sentences).
+"""
+        response_1 = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt_1}],
+            response_format={"type": "json_object"}
+        )
+        params_1 = json.loads(response_1.choices[0].message.content)
+        
+        # 2. Call DeepPersona with 200 attributes
+        result_200 = dp_client.predict(
+            age=params_1.get("Age", 30),
+            gender=params_1.get("Gender", "Female"),
+            occupation=params_1.get("Occupation", "Professional"),
+            city=params_1.get("City", "New York"),
+            country=params_1.get("Country", "USA"),
+            custom_values=params_1.get("Personal Values", "Hardworking"),
+            custom_life_attitude=params_1.get("Life Attitude", "Positive"),
+            life_story=params_1.get("Life Story", "Grew up in the city"),
+            interests_hobbies=params_1.get("Interests and Hobbies", "Reading"),
+            attribute_count=200,
+            api_name="/generate_persona"
+        )
+        
+        # 3. Use LLM to extract specific truth/details from 200 output for the 400 call
+        prompt_2 = f"""
+Based on this generated persona output:
+{result_200}
+
+Extract and enhance specific details to create an updated set of parameters. Return ONLY a valid JSON object with these EXACT keys:
+"Age" (number), "Gender" (string), "Occupation" (string), "City" (string), "Country" (string), "Personal Values" (string), "Life Attitude" (string), "Life Story" (string), "Interests and Hobbies" (string).
+"""
+        response_2 = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt_2}],
+            response_format={"type": "json_object"}
+        )
+        params_2 = json.loads(response_2.choices[0].message.content)
+        
+        # 4. Call DeepPersona with 400 attributes
+        result_400 = dp_client.predict(
+            age=params_2.get("Age", 30),
+            gender=params_2.get("Gender", "Female"),
+            occupation=params_2.get("Occupation", "Professional"),
+            city=params_2.get("City", "New York"),
+            country=params_2.get("Country", "USA"),
+            custom_values=params_2.get("Personal Values", "Hardworking"),
+            custom_life_attitude=params_2.get("Life Attitude", "Positive"),
+            life_story=params_2.get("Life Story", "Grew up in the city"),
+            interests_hobbies=params_2.get("Interests and Hobbies", "Reading"),
+            attribute_count=400,
+            api_name="/generate_persona"
+        )
+        
+        # 5. Extract final structured data for _persona output
+        prompt_3 = f"""
+Based on this final generated persona output:
+{result_400}
 
+Extract the persona details into a structured format. Return ONLY a valid JSON object with these EXACT keys:
+"name" (string, make one up if not found), "age" (number), "nationality" (string), "country_of_residence" (string), "occupation" (string), "residence" (string).
+"""
+        response_3 = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt_3}],
+            response_format={"type": "json_object"}
+        )
+        final_persona = json.loads(response_3.choices[0].message.content)
+        final_persona["full_profile_text"] = result_400
+        personas.append(final_persona)
+        
+    return personas
 def start_simulation(name, content_text, format_type, persona_count, network_type):
     config = SimulationConfig(name=name, persona_count=int(persona_count), network_type=network_type)
     sim = simulation_manager.create_simulation(config)
@@ -213,4 +295,4 @@ with gr.Blocks(css=".big-input textarea { height: 300px !important; } #mesh-netw
         gr.Button("save_focus_group").click(save_focus_group_api, inputs=[gr.Textbox(), gr.Textbox()], outputs=gr.JSON(), api_name="save_focus_group")
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(show_error=True)

patch_app.py

@@ -0,0 +1,111 @@
+import re
+
+with open('app.py', 'r') as f:
+    content = f.read()
+
+new_func = '''
+def generate_personas(business_description, customer_profile, num_personas, api_key=None):
+    if api_key:
+        os.environ["BLABLADOR_API_KEY"] = api_key
+        os.environ["OPENAI_API_KEY"] = api_key
+    
+    import json
+    from gradio_client import Client
+    import openai
+    
+    client = openai.OpenAI()
+    dp_client = Client("THzva/deeppersona-experience")
+    
+    personas = []
+    
+    for _ in range(int(num_personas)):
+        # 1. Generate initial parameters for the 200 API call
+        prompt_1 = f"""
+Given the following business description and customer profile:
+Business: {business_description}
+Customer: {customer_profile}
+
+Generate realistic parameters for a persona that fits this profile. Return ONLY a valid JSON object with these EXACT keys:
+"Age" (number), "Gender" (string), "Occupation" (string), "City" (string), "Country" (string), "Personal Values" (string), "Life Attitude" (string), "Life Story" (string), "Interests and Hobbies" (string).
+Keep the string fields concise (1-2 sentences).
+"""
+        response_1 = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt_1}],
+            response_format={"type": "json_object"}
+        )
+        params_1 = json.loads(response_1.choices[0].message.content)
+        
+        # 2. Call DeepPersona with 200 attributes
+        result_200 = dp_client.predict(
+            age=params_1.get("Age", 30),
+            gender=params_1.get("Gender", "Female"),
+            occupation=params_1.get("Occupation", "Professional"),
+            city=params_1.get("City", "New York"),
+            country=params_1.get("Country", "USA"),
+            custom_values=params_1.get("Personal Values", "Hardworking"),
+            custom_life_attitude=params_1.get("Life Attitude", "Positive"),
+            life_story=params_1.get("Life Story", "Grew up in the city"),
+            interests_hobbies=params_1.get("Interests and Hobbies", "Reading"),
+            attribute_count=200,
+            api_name="/generate_persona"
+        )
+        
+        # 3. Use LLM to extract specific truth/details from 200 output for the 400 call
+        prompt_2 = f"""
+Based on this generated persona output:
+{result_200}
+
+Extract and enhance specific details to create an updated set of parameters. Return ONLY a valid JSON object with these EXACT keys:
+"Age" (number), "Gender" (string), "Occupation" (string), "City" (string), "Country" (string), "Personal Values" (string), "Life Attitude" (string), "Life Story" (string), "Interests and Hobbies" (string).
+"""
+        response_2 = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt_2}],
+            response_format={"type": "json_object"}
+        )
+        params_2 = json.loads(response_2.choices[0].message.content)
+        
+        # 4. Call DeepPersona with 400 attributes
+        result_400 = dp_client.predict(
+            age=params_2.get("Age", 30),
+            gender=params_2.get("Gender", "Female"),
+            occupation=params_2.get("Occupation", "Professional"),
+            city=params_2.get("City", "New York"),
+            country=params_2.get("Country", "USA"),
+            custom_values=params_2.get("Personal Values", "Hardworking"),
+            custom_life_attitude=params_2.get("Life Attitude", "Positive"),
+            life_story=params_2.get("Life Story", "Grew up in the city"),
+            interests_hobbies=params_2.get("Interests and Hobbies", "Reading"),
+            attribute_count=400,
+            api_name="/generate_persona"
+        )
+        
+        # 5. Extract final structured data for _persona output
+        prompt_3 = f"""
+Based on this final generated persona output:
+{result_400}
+
+Extract the persona details into a structured format. Return ONLY a valid JSON object with these EXACT keys:
+"name" (string, make one up if not found), "age" (number), "nationality" (string), "country_of_residence" (string), "occupation" (string), "residence" (string).
+"""
+        response_3 = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt_3}],
+            response_format={"type": "json_object"}
+        )
+        final_persona = json.loads(response_3.choices[0].message.content)
+        final_persona["full_profile_text"] = result_400
+        personas.append(final_persona)
+        
+    return personas
+'''
+
+# Find the def generate_personas function block
+pattern = r"def generate_personas\(business_description, customer_profile, num_personas, api_key=None\):.*?(?=\ndef start_simulation)"
+new_content = re.sub(pattern, new_func.strip(), content, flags=re.DOTALL)
+
+with open('app.py', 'w') as f:
+    f.write(new_content)
+
+print("Patch applied")