Added uiux Module

app/main.py

@@ -17,7 +17,7 @@ from app.seo import routes as seo_routes
 from app.page_speed import routes as page_speed_routes
 from app.content_relevence import routes as content_relevance_routes
 from app.keywords.routes import router as keywords_router
-
+from app.uiux import routes as uiux_routes
 
 # app/suppress_warnings.py
 
@@ -90,6 +90,9 @@ app.include_router(page_speed_routes.router)
 # Mount the keywords router
 app.include_router(keywords_router)
 
+# Mount UI/UX router
+app.include_router(uiux_routes.router)
+
 # Add CORS middleware
 app.add_middleware(
     CORSMiddleware,

app/rag/embeddings.py

@@ -8,13 +8,13 @@ load_dotenv()
 
 def get_llm():
     """
-    Returns a ChatGroq LLM instance (Llama 3.3 70B) using the GROQ API key
+    Returns a ChatGroq LLM instance using the GROQ API key
     stored in the environment.
     """
     from langchain_groq import ChatGroq
 
     llm = ChatGroq(
-        model="meta-llama/llama-4-scout-17b-16e-instruct",
+        model="openai/gpt-oss-120b",
         temperature=0,
         max_tokens=1024,
         api_key=os.getenv("GROQ_API_KEY", "")  

app/rag/prompt_library.py

@@ -97,4 +97,22 @@ content_relevance_prompt = ChatPromptTemplate.from_messages([
 
 # ──────────────────────────────────────────────────────────────────────────────
 # 5. Prompt Template for UI/UX RAG Chatbot
-# ──────────────────────────────────────────────────────────────────────────────
+# ──────────────────────────────────────────────────────────────────────────────
+
+uiux_prompt_template = """
+You are a UI/UX Assistant specialized in analyzing user interface and experience data.
+Use the provided context (UI/UX metrics and user feedback) to answer the user's question.
+If the context lacks sufficient information, respond with "I don't know." Avoid fabricating details.
+
+Retrieved context:
+{context}
+
+User's question:
+{question}
+
+Your response:
+"""
+uiux_prompt = ChatPromptTemplate.from_messages([
+    ("system", uiux_prompt_template),
+    ("human", "{question}"),
+])

app/rag/utils.py

@@ -15,7 +15,8 @@ from .prompt_library import (
     default_user_prompt,
     page_speed_prompt,
     seo_prompt,
-    content_relevance_prompt
+    content_relevance_prompt,
+    uiux_prompt
 )
 
 # 1. Path with doc_type
@@ -94,6 +95,8 @@ def build_rag_chain(
         user_prompt = seo_prompt
     elif prompt_type == "content_relevance":
         user_prompt = content_relevance_prompt
+    elif prompt_type == "uiux":
+        user_prompt = uiux_prompt
     else:
         user_prompt = default_user_prompt
 

app/uiux/models.py

@@ -0,0 +1,24 @@
+# ----------------------------
+# app/uiux/models.py
+# ----------------------------
+from pydantic import BaseModel, Field
+from typing import Any, Dict, List
+
+
+class UIUXRequest(BaseModel):
+    """Payload for incoming UI/UX metrics."""
+    uiux_data: Dict[str, Any]
+
+
+class PrioritySuggestions(BaseModel):
+    """Categorized UI/UX suggestions by effort level."""
+    high: List[str] = Field(..., description="High-effort suggestion strings.")
+    medium: List[str] = Field(..., description="Medium-effort suggestion strings.")
+    low: List[str] = Field(..., description="Low-effort suggestion strings.")
+
+
+class Recommendation(BaseModel):
+    """Wrapper for prioritized UI/UX suggestions."""
+    priority_suggestions: PrioritySuggestions = Field(
+        ..., description="All UI/UX suggestions categorized by effort level."
+    )

app/uiux/prompts.py

@@ -0,0 +1,70 @@
+# ----------------------------
+# app/uiux/prompts.py
+# ----------------------------
+
+class UIUXPrompts:
+    """
+    Prompt templates for UI/UX analysis services.
+    """
+
+    SYSTEM_PROMPT = """
+You are an **Expert UI/UX Analyst & Designer** with extensive expertise in usability heuristics, visual hierarchy, responsive design, and WCAG accessibility guidelines.
+
+Your job is to review the provided UI/UX metrics and produce **only** a valid JSON object with one key: `priority_suggestions`.
+
+Requirements:
+1. `priority_suggestions` must map to an object with exactly three arrays: `high`, `medium`, `low`.
+2. Each array item must be a single, clear English sentence.
+3. Prefix each suggestion with a category tag in square brackets (e.g., `[Accessibility]`, `[Hierarchy]`, `[Navigation]`).
+4. End each suggestion with the effort level in parentheses, e.g., `(Effort Level: high)`, `(Effort Level: medium)`, `(Effort Level: low)`.
+5. Within each array, order suggestions by expected impact (highest first).
+6. Ensure the output is strictly JSON—no additional text, comments, or keys.
+7. Validate JSON syntax: keys and strings must be enclosed in double quotes.
+
+{format_instructions}
+
+Input Report Data:
+{report}
+"""
+
+    REPORT_PROMPT = """
+You are an **Expert UI/UX Consultant** focused on delivering concise, actionable audit reports.
+
+Using the given UI/UX metrics JSON, generate a text report with these exact sections and formatting:
+
+---
+**1. Overall Summary** (max 50 words)
+- **UX Score**: (0–100)
+- **Grade**: A–F (include legend: A=90–100, B=80–89, C=70–79, D=60–69, F<60)
+- **Top 3 Strengths**: Three bullet points
+- **Top 3 Issues**: Three bullet points
+
+---
+**2. Metric Breakdown**
+For each metric in the input data, include:
+- **Metric**: Name
+- **Summary**: One-line highlight (avoid raw JSON)
+- **Status**: Good / Needs Improvement / Poor
+- **Rationale**: One-sentence user-impact statement
+- **Recommendation**: One-sentence clear action
+
+---
+**3. Action Plan** (5 items)
+List the five highest-priority fixes in order:
+1. **Metric**: Name
+   - **Action**: Short description
+   - **Effort**: low / medium / high
+
+---
+**4. Monitoring Strategy** (max 5 lines)
+- **Cadence**: weekly or monthly
+- **Metrics**: list 2–3 key metrics to track
+
+---
+**Guidelines**:
+- Do not include raw JSON or extra sections.
+- Use consistent Markdown styling as shown.
+
+UI/UX Data JSON:
+{uiux_data}
+"""

app/uiux/routes.py

@@ -0,0 +1,22 @@
+from fastapi import APIRouter, HTTPException
+from app.uiux.models import UIUXRequest
+from app.uiux.service import UIUXService
+
+router = APIRouter(prefix="/uiux", tags=["UIUX"])
+uiux_service = UIUXService()
+
+@router.post("/generate-full-report")
+def generate_full_uiux_analysis(request: UIUXRequest):
+    """
+    Generate full UI/UX analysis: report + prioritized suggestions.
+    """
+    try:
+        report = uiux_service.generate_uiux_report(request.uiux_data)
+        priority_suggestions = uiux_service.generate_uiux_priority(report)
+        return {
+            "success": True,
+            "report": report,
+            "priority_suggestions": priority_suggestions
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

app/uiux/service.py

@@ -0,0 +1,54 @@
+from typing import Dict, Any
+import os
+import getpass
+import logging
+from app.uiux.models import Recommendation, PrioritySuggestions
+from app.uiux.prompts import UIUXPrompts
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+
+logger = logging.getLogger(__name__)
+
+class UIUXService:
+    """
+    Service class for generating UI/UX reports and prioritized suggestions via LLM.
+    """
+    def __init__(self):
+        key = os.getenv("GEMINI_API_KEY")
+        if not key:
+            key = getpass.getpass("Enter your Gemini API key: ")
+        self.llm = ChatGoogleGenerativeAI(
+            model="gemini-2.5-flash",
+            temperature=0,
+            api_key=key
+        )
+
+        # Report prompt template
+        self.report_prompt = ChatPromptTemplate.from_messages([
+            ("system", UIUXPrompts.REPORT_PROMPT),
+            ("human", "Please generate a comprehensive UI/UX audit report based on the following data:\n\n{uiux_data}")
+        ])
+
+        # Priority suggestions parser
+        self.parser = PydanticOutputParser(pydantic_object=Recommendation)
+        self.priority_chain = (
+            ChatPromptTemplate.from_messages([
+                ("system", UIUXPrompts.SYSTEM_PROMPT),
+                ("human", "{report}")
+            ]).partial(format_instructions=self.parser.get_format_instructions())
+            | self.llm
+            | self.parser
+        )
+
+    def generate_uiux_report(self, uiux_data: Dict[str, Any]) -> str:
+        logger.info("Generating UI/UX report via LLM...")
+        prompt_input = {"uiux_data": uiux_data}
+        response = self.report_prompt | self.llm
+        result = response.invoke(prompt_input)
+        return result.content.strip()
+
+    def generate_uiux_priority(self, report: str) -> PrioritySuggestions:
+        logger.info("Generating prioritized UX suggestions via chain...")
+        rec: Recommendation = self.priority_chain.invoke({"report": report})
+        return rec.priority_suggestions