Deployemnt retry

Dockerfile

@@ -10,19 +10,19 @@ WORKDIR /app
 COPY requirements.txt .
 
 RUN pip install --no-cache-dir -r requirements.txt
-RUN python -m playwright install --with-deps chromium 
+# ENV PLAYWRIGHT_BROWSERS_PATH=/ms-playwright
+# RUN mkdir -p /ms-playwright
+RUN python -m playwright install --with-deps chromium
+# RUN chmod -R a+rX /ms-playwright
 
 RUN useradd -m -u 1000 user
 USER user
 
 COPY --chown=user . /app
 
-EXPOSE 7680
-
-ENV PORT=7680
 ENV PYTHONUNBUFFERED=1
 
 RUN chmod -R a+rX /ms-playwright || true
 
 # RUN uvicorn app_gui:app --host 0.0.0.0 --port $PORT 
-CMD ["uvicorn", "app_gui:app", "--host", "0.0.0.0", "--port", "7680"]
+CMD ["uvicorn", "app_gui:app", "--host", "0.0.0.0", "--port", "8001", "--proxy-headers" , "--forwarded-allow-ips=*"]

README.md

@@ -6,67 +6,5 @@ colorTo: red
 sdk: docker
 pinned: false
 license: unknown
-short_description: Shopping Assistant built using Pydantic AI & Google Gemini L
----
-
-# Shopping Assistant 
-Shopping Assistant built using Pydantic AI & Google Gemini LLM. 
-
-## Demo Video
-
-https://github.com/user-attachments/assets/9c60d0e7-a388-4da1-8e8d-58cb99d0af10
-
-Helps : 
-- Create the optimal search query 
-- Retrieve the available filters for the product on the website 
-- Filter out products based on User's preferences 
-- Recommend the most suitable product as well as the top 10 candidates
-
-## Table of Contents
-- Introduction
-- Demo Video
-- Setup
-- Workflow
-- Results
-
-## Introduction:
-It is an agentic system built using Pydantic AI & Pydantic for data validation . All data is consistently validated. Google Gemini 2.5 Flash lite is used as the base LLM & flipkart is chosen as the shopping site currently. The agent has access to various tools :  
-            - get_pro_class: Extract the product category & type from the user's query.
-            - prompt_user0: Prompt the user for further details about the product.
-            - rephrase_query : Generate a very concise product search query - combining original query & user provided details
-            - get_best_site: Find the best website to search for the product.
-            - get_site_filters: Retrieve all the available filters on the best site for the product.
-            - prompt_user1: Prompt the user for further details about the product using the available filters
-            - get_candidates : Return the best candidates found for the product 
-& chooses them depending on the workflow , user's demands & its own logic.
-
-Ideal Workflow: 
-
-```mermaid
-graph TD;
-  A[User Input] --> B[Agent];
-  B --> C[Product Class];
-  B --> D[Prompt User for Product Details];
-  B --> E[Optimize Search Query with All Details];
-  B --> F[Get Best Site];
-  B --> G[Get All Site Filters];
-  B --> H[Prompt User for Which Filters to Use];
-  B --> I[Search, Filter & Return Products];
-```
-
-
-## Setup
-To run the project locally: 
-
-1.Install Python dependencies:
-`pip install -r requirements.txt`
-
-2.Clone the repository:
-`git clone https://github.com/eshan1347/shop_agent`
-
-3.Run:
-`python pydantic_ai_agents.py`
-
-## Results
-
-This agentic system can be further improved by increasing the number of tools available - so that even more freedom is afforded to the agent. Web scraping data from websites add too much latency to the system - will further explore if any API's are available . Various other sites will be added so that products from various sites can be retrieved & a more holistic recommendation can be returned. 
+short_description: Shopping Assistant built using Pydantic AI & Google Gemini LLM
+---