v2 initial

agents/analysis_phase/Introduction.py

@@ -97,15 +97,19 @@ intro_task = Task(
         "• الإفراط في الجمل الطويلة.\n"
         "• العبارات التسويقية.\n"
         "• الكلمات اللامعة الخاوية.\n\n"
-        "## OUTPUT\n"
-        "Return JSON:\n"
-        "{\n"
-        '  "training_introduction": "النص العربي الكامل"\n'
-        "}\n"
+        # "## OUTPUT\n"
+        # "Return JSON:\n"
+        # "{\n"
+        # '  "introduction": "النص العربي الكامل"\n'
+        # "}\n"
     ),
-    expected_output="A JSON object in Arabic containing introduction.",
-    #output_json=IntroductionOutput,
-    #output_parser=parsed_introduction,
+    expected_output="""A JSON object in Arabic containing introduction with this structure:
+    {
+    "introduction": "engaging introduction to the training program content, max 300 words."
+    }
+    """,
+    output_json=IntroductionOutput,
+    # output_parser=parsed_introduction,
     agent=intro_agent,
     human_input=False,
 )

routers/introduction_route.py

@@ -15,8 +15,6 @@ from schemas import (
     LearningOutcomesOutput,
     DNAMetadata,
     OutlineInput,
-    SafePydanticParser,
-    IntroductionOutput
 )
 
 
@@ -32,7 +30,7 @@ def run_training(
     data: OutlineInput,
     outlines: ValidatedCurriculumOutput,
     outcomes: LearningOutcomesOutput,
-    units: dict,
+    units:dict,
 ):
     training_crew = Crew(
         agents=[intro_agent],
@@ -55,9 +53,7 @@ def run_training(
         "outcomes": outcomes.dict(),
         "units": units,
     }
-    parser = SafePydanticParser(IntroductionOutput)
     result = training_crew.kickoff(inputs=merged_inputs)
-    structured = parser.parse(result)
-    print(structured.json_dict)
+    print(result.json_dict)
 
-    return {"message": "Introduction Generated Well 🚀", "result": structured}
+    return {"message": "Introduction Generated Well 🚀", "result": result}

schemas/__init__.py

@@ -15,9 +15,5 @@ from .outliner_schema import (
 )
 
 from .source_schema import AllResults, SingleResult
-from .introduction_schema import (
-    IntroductionOutput,
-    parsed_introduction,
-    SafePydanticParser,
-)
+from .introduction_schema import IntroductionOutput
 from .inputs_schema import OutlineInput

schemas/introduction_schema.py

@@ -1,50 +1,10 @@
-from pydantic import BaseModel, Field, ValidationError
+from pydantic import BaseModel, Field
 from typing import List, Dict, Literal, Optional
-import re
-import json
-
-CONTROL_CHARS = "".join(map(chr, range(0, 32)))
-CONTROL_CHAR_RE = re.compile(f"[{re.escape(CONTROL_CHARS)}]")
-
-
-import re
-import json
-from pydantic import ValidationError
-
-
-class SafePydanticParser:
-    def __init__(self, model):
-        self.model = model
-
-    def parse(self, raw_output: str):
-        # 1. Remove control characters
-        cleaned = re.sub(r"[\x00-\x1F\x7F]", "", raw_output)
-
-        # 2. Extract JSON block
-        match = re.search(r"\{.*\}", cleaned, re.S)
-        if not match:
-            raise ValueError("❌ No JSON object found!")
-
-        json_block = match.group()
-
-        # 3. Validate JSON structure before sending to Pydantic
-        try:
-            json.loads(json_block)
-        except json.JSONDecodeError as e:
-            raise ValueError(f"❌ JSON is malformed: {e}")
-
-        # 4. Convert to Pydantic
-        try:
-            return self.model.model_validate_json(json_block)
-        except ValidationError as e:
-            raise ValueError(f"❌ Pydantic validation error: {e}")
 
 
 class IntroductionOutput(BaseModel):
-    training_introduction: str = Field(
+    introduction: str = Field(
         ...,
-        title="training_introduction",
+        title="Introduction",
         description="engaging introduction to the training program content, max 300 words.",
     )
-
-parsed_introduction = SafePydanticParser(IntroductionOutput)