Added Mobile Usability Module

.dockerignore

@@ -0,0 +1,11 @@
+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.env
+.git
+.gitignore
+Dockerfile
+.dockerignore

Dockerfile

@@ -4,14 +4,6 @@ FROM python:3.11-slim
 # Prevent Python from buffering stdout/stderr (so logs appear immediately)
 ENV PYTHONUNBUFFERED=1
 
-# Install system dependencies needed for certain Python packages (e.g., faiss, PyTorch CPU wheels)
-RUN apt-get update && \
-    apt-get install -y --no-install-recommends \
-        build-essential \
-        git \
-        libgomp1 \
-    && rm -rf /var/lib/apt/lists/*
-
 # Set the working directory inside the container
 WORKDIR /app
 
@@ -29,4 +21,4 @@ COPY . .
 EXPOSE 8080
 
 # Run the app on port 8080 to match Cloud Run's requirement
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]

app/main.py

@@ -18,7 +18,7 @@ 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
-
+from app.mobile_usability import routes as mobile_usability
 # app/suppress_warnings.py
 
 import warnings
@@ -93,6 +93,8 @@ app.include_router(keywords_router)
 # Mount UI/UX router
 app.include_router(uiux_routes.router)
 
+app.include_router(mobile_usability.router)
+
 # Add CORS middleware
 app.add_middleware(
     CORSMiddleware,

app/mobile_usability/models.py

@@ -0,0 +1,24 @@
+# app/mobile_usability/models.py
+from pydantic import BaseModel, Field
+from typing import Any, Dict, List
+
+
+class MobileUsabilityRequest(BaseModel):
+    """
+    Payload for incoming Mobile Usability data.
+    """
+    mobile_data: Dict[str, Any]
+
+
+class PrioritySuggestions(BaseModel):
+    """Categorized suggestions by effort level for mobile usability."""
+    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 suggestions returned by the LLM parser."""
+    priority_suggestions: PrioritySuggestions = Field(
+        ..., description="All suggestions categorized by effort level."
+    )

app/mobile_usability/prompts.py

@@ -0,0 +1,128 @@
+# app/mobile_usability/prompts.py
+
+class MobilePrompts:
+    """
+    Prompt templates for mobile usability analysis (improved).
+    """
+
+    # This system prompt is used to generate the structured priority suggestions JSON
+    SYSTEM_PROMPT = """
+You are an **Expert Mobile Usability Analyst** and an authoritative accessibility specialist.
+
+Task:
+Analyze the provided mobile usability audit (multi-line report) and extract a short,
+prioritized list of remediation suggestions. **Return only a JSON object** with a single
+top-level key `priority_suggestions` whose value is an object containing exactly three lists:
+- "high"
+- "medium"
+- "low"
+
+Requirements for each list item (each item is a single string):
+- Begin with a concise category tag in square brackets (e.g. [Viewport], [Tap Targets], [Font], [Layout], [Accessibility]).
+- Include a one-sentence remediation recommendation (plain English).
+- End with the parenthetical suffix `(Effort Level: high|medium|low; Est: Xm)` where `Xm` is an estimated time in minutes (approx).
+- Optionally include a short rationale phrase before the parenthetical suffix.
+
+Example list item:
+"[Font] Fix CSS rule that sets font-size: 0px for social links; set readable font-size and ARIA labels (Effort Level: high; Est: 30m)"
+
+Formatting rules:
+- Return **only** the JSON object (no commentary, no surrounding text).
+- Each list may contain zero or more items, but critical items must appear in "high".
+- Ensure items are specific enough for a developer to action (mention affected selector(s) when possible).
+
+{format_instructions}
+
+Use the following to guide prioritization:
+- **High** = content invisible (0px), severe accessibility issues, or site-breaking layout on mobile.
+- **Medium** = important usability issues (many small tap targets, repeated spacing problems).
+- **Low** = cosmetic or easily reversible issues (minor font-size tweaks, single button padding).
+
+Mobile Usability Report (multi-line):
+{report}
+    """
+
+    # This prompt is used to generate the human-readable multi-line audit report (the main report)
+    Report_PROMPT = """
+You are an **Expert Mobile Usability Consultant** and must produce a clear, technical, and actionable mobile usability audit.
+
+Output rules (must be followed exactly):
+- Return a **multi-line string** (NOT JSON).
+- The report **must begin** with a line containing only three dashes `---` and **end** with a line containing only three dashes `---`.
+- Do **not** include any other text before the starting `---` or after the ending `---`.
+- Do not provide any meta commentary, step-by-step generation notes, or extraneous text — only the report content.
+
+Structure the report with the following sections and content (in this exact order):
+
+---
+
+**Overall Summary**
+- Usability Score: (0–100) — echo the numeric score.
+- Grade: A/B/C/D/F — map score to grade using: 90–100=A, 80–89=B, 70–79=C, 60–69=D, <60=F.
+- Top Strengths: list the top 3 strengths (1 line each).
+- Top Issues: list the top 3 issues (1 line each), and **tag** each issue with its severity (Critical / High / Medium / Low).
+
+Include an immediate "Quick wins (actionable now)" sub-list containing 1–3 items that a developer can fix in ≤30 minutes.
+
+---
+
+**Metric Breakdown**
+For each available metric (missingViewport, horizontalScroll, smallTapTargets, fontSizeReadability, and any others present in the JSON), include a subsection with these fields:
+
+- Metric Name:
+- Value: (echo input value; for objects, provide a short summary and include counts: e.g., "13 flagged elements")
+- Status: good / needs improvement / critical (pick one)
+- Why It Matters: 1–2 concise sentences
+- Concrete Recommendation: 1–2 sentences explaining the fix
+- Affected Selectors: list up to 10 CSS selectors from the input (if any). If selectors are not available, say "Selectors not provided."
+- Sample Fix (when applicable): show a minimal copy-pastable snippet (CSS or HTML) that addresses the issue — keep snippets ≤8 lines.
+- Acceptance Criteria: one or two measurable pass/fail checks (e.g., "smallTapTargets score >= 90", "no elements with computed font-size: 0px", "no horizontal scroll in Chrome device emulation at viewport widths 360–412px").
+- Verification Steps: concise commands or DevTools steps to confirm the fix (e.g., Lighthouse CLI: `lighthouse --only-categories=best-practices,accessibility --throttling-method=devtools https://example.com` or DevTools computed style checks).
+
+Keep each metric subsection compact and developer-focused.
+
+---
+
+**Action Plan (Priority + Effort + ETA)**
+List the top 5 issues in prioritized order. For each item include:
+- Issue Title (Severity)
+  - Root cause (one line)
+  - Fix (one-line summary)
+  - Code example (if applicable) — ≤6 lines
+  - Effort Level: low / medium / high
+  - Estimated Time: Xm (minutes)
+  - Acceptance Criteria: (clear measurable outcome)
+  - Verification: (one short command or DevTools action)
+
+Ensure at least one item in **High** priority if any content is invisible (0px) or there is a major accessibility problem.
+
+---
+
+**Selector-level Appendix**
+- Provide a small table/listing of flagged selectors and a one-line note each (e.g., `#e-n-tab-title-31374311 — increase min-height to 48px`).
+- If the input includes many selectors, list the first 20 and summarize the rest as "N more selectors".
+
+---
+
+**Monitoring & Regression Tests**
+- Frequency recommendation based on severity (weekly vs monthly).
+- Automated checks to add to CI (1–3 bullet suggestions, include sample Lighthouse CLI command).
+- Acceptance gates to include in PRs (e.g., "no elements with computed font-size: 0px", "smallTapTargets score >= 90").
+
+---
+
+**Scoring & Grade Explanation**
+- Explain briefly how the grade was determined (one short paragraph).
+- Provide numeric thresholds used to mark items "critical", "needs improvement", or "good".
+
+---
+
+Additional guidance:
+- Use explicit, actionable language aimed at a frontend developer.
+- When recommending CSS fixes, prefer modern and resilient approaches (use `min-height`, `padding`, relative font units, and accessible labels).
+- For accessibility issues (e.g., invisible content), always mark severity as Critical/High and include an accessibility rationale.
+- Keep the report concise overall (try to keep it under ~700–900 words for normal-sized inputs), but do include code snippets and selectors as required.
+
+Mobile usability data (JSON):
+{mobile_data}
+    """

app/mobile_usability/routes.py

@@ -0,0 +1,29 @@
+# app/mobile_usability/routes.py
+from fastapi import APIRouter, HTTPException
+from app.mobile_usability.service import MobileUsabilityService
+from app.mobile_usability.models import MobileUsabilityRequest
+
+router = APIRouter(prefix="/mobile_usability", tags=["MobileUsability"])
+
+service = MobileUsabilityService()
+
+
+@router.post("/generate-full-report")
+def generate_full_mobile_analysis(request: MobileUsabilityRequest):
+    """
+    Generate full Mobile Usability analysis using Gemini: report + prioritized suggestions.
+    """
+    try:
+        # 1) Generate report (string) via LLM
+        report = service.generate_mobile_report(request.mobile_data)
+
+        # 2) Generate prioritized suggestions via LLM (Pydantic parser)
+        priority_suggestions = service.generate_mobile_priority(report)
+
+        return {
+            "success": True,
+            "report": report,
+            "priority_suggestions": priority_suggestions
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

app/mobile_usability/service.py

@@ -0,0 +1,98 @@
+# app/mobile_usability/service.py
+import os
+import logging
+from typing import Dict, Any
+
+# Optional project settings import (falls back to env var)
+try:
+    from app.page_speed.config import settings  # if present in your project
+    GEMINI_KEY = getattr(settings, "gemini_api_key", None)
+except Exception:
+    GEMINI_KEY = None
+
+from app.mobile_usability.models import Recommendation, PrioritySuggestions
+from app.mobile_usability.prompts import MobilePrompts
+
+# LangChain / Gemini wrapper imports (same style as your SEO module)
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+
+glogger = logging.getLogger(__name__)
+
+
+class MobileUsabilityService:
+    """
+    LLM-only service for generating mobile usability report and prioritized suggestions.
+    This class requires a Gemini API key to be available (env var or settings).
+    """
+
+    def __init__(self):
+        # require Gemini key
+        key = GEMINI_KEY or os.getenv("GEMINI_API_KEY")
+        if not key:
+            msg = "Gemini API key not configured. Set settings.gemini_api_key or GEMINI_API_KEY env var."
+            glogger.error(msg)
+            raise RuntimeError(msg)
+
+        self.gemini_api_key = key
+
+        # initialize LLM wrapper (Gemini)
+        self.llm = ChatGoogleGenerativeAI(
+            model="gemini-2.5-flash",
+            temperature=0,
+            max_tokens=None,
+            timeout=None,
+            max_retries=3,
+            api_key=self.gemini_api_key
+        )
+
+        # Prompt template for generating the multi-line mobile usability report
+        self.report_prompt = ChatPromptTemplate.from_messages([
+            ("system", MobilePrompts.Report_PROMPT),
+            ("human", "Please generate a comprehensive mobile usability audit based on this data:\n\n{mobile_data}")
+        ])
+
+        # Parser + priority prompt for structured priority suggestions
+        self.parser = PydanticOutputParser(pydantic_object=Recommendation)
+        self.priority_chain = (
+            ChatPromptTemplate.from_messages([
+                ("system", MobilePrompts.SYSTEM_PROMPT),
+                ("human", "{report}")
+            ]).partial(format_instructions=self.parser.get_format_instructions())
+            | self.llm
+            | self.parser
+        )
+
+    def generate_mobile_report(self, mobile_data: Dict[str, Any]) -> str:
+        """
+        Generate a mobile usability audit report using Gemini LLM (raw multi-line string).
+        Raises on failure.
+        """
+        glogger.info("Invoking Gemini to generate mobile usability report.")
+        prompt_input = {"mobile_data": mobile_data}
+        try:
+            # Use the prompt template piped into the LLM (same pattern as your SEO module)
+            prompt_with_llm = self.report_prompt | self.llm
+            response = prompt_with_llm.invoke(prompt_input)
+            if not response:
+                raise RuntimeError("Empty response from Gemini for mobile usability report.")
+            # response is typically an object with .content in your setup; keep same handling
+            return getattr(response, "content", str(response)).strip()
+        except Exception as e:
+            glogger.error("Error generating mobile report via Gemini: %s", e, exc_info=True)
+            raise
+
+    def generate_mobile_priority(self, report: str) -> PrioritySuggestions:
+        """
+        Generate prioritized mobile-usability suggestions using LLM + PydanticOutputParser.
+        Returns PrioritySuggestions Pydantic model instance.
+        """
+        glogger.info("Invoking Gemini to generate priority suggestions.")
+        try:
+            rec: Recommendation = self.priority_chain.invoke({"report": report})
+            # `rec` is a Pydantic model (Recommendation); return the nested PrioritySuggestions
+            return rec.priority_suggestions
+        except Exception as e:
+            glogger.error("Error generating priorities via Gemini: %s", e, exc_info=True)
+            raise

app/page_speed/config.py

@@ -11,7 +11,6 @@ class Settings(BaseSettings):
     # ───────────────────────────────────────────────────────────────────────────
     pagespeed_api_key: str
     gemini_api_key: str
-    google_api_key1: str
 
 
     # ───────────────────────────────────────────────────────────────────────────
@@ -39,13 +38,13 @@ class Settings(BaseSettings):
         pw = quote_plus(self.mongo_password)
         return (
             f"mongodb+srv://{self.mongo_user}:{pw}@{self.mongo_host}/"
-            f"{self.mongo_db}?retryWrites=true&w=majority"
+            f"{self.mongo_db}?retryWrites=true&w=majority&ssl=true"
         )
     # ───────────────────────────────────────────────────────────────────────────
     # FastAPI Server Configuration
     # ───────────────────────────────────────────────────────────────────────────
     host: str = "0.0.0.0"
-    port: int = int(os.getenv("PORT", 8080))
+    port: int = os.getenv("port")
     debug: bool = False
 
     # ───────────────────────────────────────────────────────────────────────────

app/rag/db.py

@@ -7,7 +7,8 @@ from app.page_speed.config import settings
 # ──────────────────────────────────────────────────────────────────────────────
 
 # Connect to MongoDB using the URI from settings
-mongo_client = MongoClient(settings.mongo_uri)
+mongo_client = MongoClient("localhost", 27017)  # Use default MongoDB port
+# mongo_client = MongoClient(settings.mongo_uri)
 
 # Use the renamed settings attributes
 mongo_db = mongo_client[settings.mongo_db]

app/rag/embeddings.py

@@ -31,15 +31,32 @@ text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=100
 # ──────────────────────────────────────────────────────────────────────────────
 
 
-HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+# HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
 
-from huggingface_hub import login
+# from huggingface_hub import login
 
-login(HF_TOKEN)
+# login(HF_TOKEN)
 
-model_name = "BAAI/bge-small-en-v1.5"
-model_kwargs = {"device": "cpu"}
-encode_kwargs = {"normalize_embeddings": True}
-embeddings = HuggingFaceEmbeddings(
-    model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
+# model_name = "BAAI/bge-small-en-v1.5"
+# model_kwargs = {"device": "cpu"}
+# encode_kwargs = {"normalize_embeddings": True}
+# embeddings = HuggingFaceEmbeddings(
+#     model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
+# )
+    
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 2. Embeddings Model (Google Gemini)
+# ──────────────────────────────────────────────────────────────────────────────
+GOOGLE_API_KEY = os.getenv("GEMINI_API_KEY")
+if not GOOGLE_API_KEY:
+    raise ValueError("GOOGLE_API_KEY is not set in environment variables")
+
+embeddings = GoogleGenerativeAIEmbeddings(
+    model="models/gemini-embedding-001",
+    google_api_key=GOOGLE_API_KEY
 )

app/rag/prompt_library.py

@@ -115,4 +115,26 @@ Your response:
 uiux_prompt = ChatPromptTemplate.from_messages([
     ("system", uiux_prompt_template),
     ("human", "{question}"),
+])
+
+# ──────────────────────────────────────────────────────────────────────────────
+# 6. Prompt Template for Mobile Usability RAG Chatbot
+# ──────────────────────────────────────────────────────────────────────────────
+
+mobile_usability_prompt_template = """
+You are a Mobile Usability Assistant specialized in analyzing mobile user experience data.
+Use the provided context (mobile usability 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:
+"""
+mobile_usability_prompt = ChatPromptTemplate.from_messages([
+    ("system", mobile_usability_prompt_template),
+    ("human", "{question}"),
 ])

app/rag/routes.py

@@ -20,7 +20,7 @@ router = APIRouter(prefix="/rag", tags=["rag"])
 @router.post("/initialization/{onboarding_id}/{doc_type}", response_model=SetupResponse)
 async def setup_rag_session(
     onboarding_id: str = Path(..., description="Unique onboarding identifier"),
-    doc_type: str = Path(..., description="Type of document (e.g., page_speed, seo, content_relevance or uiux)"),
+    doc_type: str = Path(..., description="Type of document (e.g., page_speed, seo, content_relevance, uiux or mobile_usability)"),
     body: SetupRequest = ...
 ):
     """
@@ -86,7 +86,7 @@ async def chat_with_user(
     onboarding_id: str = Path(...),
     doc_type: str = Path(...),
     chat_id: str = Path(...),
-    prompt_type: str = Query(..., description="Prompt type, e.g., page_speed, content_relevance or seo"),
+    prompt_type: str = Query(..., description="Prompt type, e.g., page_speed, content_relevance, seo, uiux or mobile_usability"),
     body: ChatRequest = ...
 ):
     """

app/rag/utils.py

@@ -16,7 +16,8 @@ from .prompt_library import (
     page_speed_prompt,
     seo_prompt,
     content_relevance_prompt,
-    uiux_prompt
+    uiux_prompt,
+    mobile_usability_prompt
 )
 
 # 1. Path with doc_type
@@ -97,6 +98,8 @@ def build_rag_chain(
         user_prompt = content_relevance_prompt
     elif prompt_type == "uiux":
         user_prompt = uiux_prompt
+    elif prompt_type == "mobile_usability":
+        user_prompt = mobile_usability_prompt
     else:
         user_prompt = default_user_prompt
 

requirements.txt

@@ -10,7 +10,4 @@ langchain_community
 faiss-cpu 
 pymongo 
 langchain-mongodb
-huggingface_hub
-sentence_transformers
 langchain_google_genai
-langchain_huggingface