Massive UX overhaul, BYOK support, and interactive fact-checking chat

README.md

@@ -167,6 +167,7 @@ GROQ_API_KEY= <groq_api_key>
 PINECONE_API_KEY = <your_pinecone_API_KEY>
 PORT = 8000
 SEARCH_KEY = <your_Google_custom_search_engine_API_key>
+HF_TOKEN = <your_huggingface_access_token>
   ```
 
 *Run backend:*

backend/.env.example

@@ -0,0 +1,7 @@
+GROQ_API_KEY=
+GROQ_MODEL=llama-3.3-70b-versatile
+PINECONE_API_KEY =
+PORT = 5555
+HF_TOKEN=
+GEMINI_MODEL=
+GEMINI_API_KEY=

backend/app/llm_config.py

@@ -0,0 +1,43 @@
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+
+LLM_MODEL = os.getenv("GROQ_MODEL_NAME", "llama-3.3-70b-versatile")
+
+
+def get_llm(provider: str = "groq", temperature: float = 0.7):
+    """Return a LangChain chat model for the requested provider.
+
+    Supported providers: ``"groq"`` (default), ``"gemini"``.
+    API keys and model names are read from environment variables so that
+    users can bring their own keys (BYOK).
+    """
+
+    if provider == "gemini":
+        from langchain_google_genai import ChatGoogleGenerativeAI
+
+        api_key = os.getenv("GEMINI_API_KEY")
+        model_name = os.getenv("GEMINI_MODEL_NAME", "gemini-2.5-flash")
+        if not api_key:
+            raise ValueError(
+                "GEMINI_API_KEY environment variable is required for Gemini"
+            )
+        return ChatGoogleGenerativeAI(
+            model=model_name,
+            google_api_key=api_key,
+            temperature=temperature,
+        )
+
+    # Default → Groq
+    from langchain_groq import ChatGroq
+
+    api_key = os.getenv("GROQ_API_KEY")
+    model_name = os.getenv("GROQ_MODEL_NAME", "llama-3.3-70b-versatile")
+    if not api_key:
+        raise ValueError("GROQ_API_KEY environment variable is required for Groq")
+    return ChatGroq(
+        model=model_name,
+        api_key=api_key,
+        temperature=temperature,
+    )

backend/app/modules/bias_detection/check_bias.py

@@ -27,6 +27,7 @@
 from dotenv import load_dotenv
 import json
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
 
 logger = setup_logger(__name__)
 
@@ -61,7 +62,7 @@ def check_bias(text):
                     "content": (f"Give bias score to the following article \n\n{text}"),
                 },
             ],
-            model="gemma2-9b-it",
+            model=LLM_MODEL,
             temperature=0.3,
             max_tokens=512,
         )

backend/app/modules/chat/chat_graph.py

@@ -0,0 +1,97 @@
+"""
+chat_graph.py
+-------------
+LangGraph-based conversational agent with persistent memory.
+"""
+
+from typing import Annotated
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import add_messages
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_core.messages import SystemMessage, HumanMessage
+from typing_extensions import TypedDict
+from app.llm_config import get_llm
+from app.logging.logging_config import setup_logger
+
+logger = setup_logger(__name__)
+
+
+class ChatState(TypedDict):
+    messages: Annotated[list, add_messages]
+
+
+_memory = MemorySaver()
+
+
+def _chatbot_node(state: ChatState, config: dict):
+    provider = config.get("configurable", {}).get("provider", "groq")
+    llm = get_llm(provider=provider, temperature=0.7)
+    response = llm.invoke(state["messages"])
+    return {"messages": [response]}
+
+
+_graph = StateGraph(ChatState)
+_graph.add_node("chatbot", _chatbot_node)
+_graph.add_edge(START, "chatbot")
+_graph.add_edge("chatbot", END)
+chat_app = _graph.compile(checkpointer=_memory)
+
+
+async def initialize_chat_thread(thread_id: str, analysis_result: dict) -> None:
+    perspective_obj = analysis_result.get("perspective", {})
+    if hasattr(perspective_obj, "model_dump"):
+        p = perspective_obj.model_dump()
+    elif hasattr(perspective_obj, "dict"):
+        p = perspective_obj.dict()
+    elif isinstance(perspective_obj, dict):
+        p = perspective_obj
+    else:
+        p = {"perspective": str(perspective_obj)}
+
+    facts = analysis_result.get("facts", [])
+    facts_text = "\n".join(
+        f"- {f.get('claim', 'N/A')}: {f.get('status', '?')} -- {f.get('reason', '')}"
+        for f in facts
+    ) or "No facts were verified."
+
+    citations = analysis_result.get("web_search_citations", [])
+    citations_text = "\n".join(
+        f"- {c.get('title', 'Untitled')} ({c.get('url', '')})"
+        for c in citations
+    ) or "No citations available."
+
+    summary = analysis_result.get("article_summary", "No summary available.")
+    sentiment = analysis_result.get("sentiment", "unknown")
+    perspective_text = p.get("perspective", "")
+
+    system_content = (
+        "You are an AI assistant helping the user understand and discuss a "
+        "news article that has been analyzed. Here is the full analysis:\n\n"
+        f"**Article Summary:**\n{summary}\n\n"
+        f"**Detected Sentiment:** {sentiment}\n\n"
+        f"**Counter-Perspective:**\n{perspective_text}\n\n"
+        f"**Fact-Check Results:**\n{facts_text}\n\n"
+        f"**Web Search Citations:**\n{citations_text}\n\n"
+        "Guidelines:\n"
+        "- Answer questions using this context.\n"
+        "- Be balanced and cite facts when relevant.\n"
+        "- Be transparent if something is not covered.\n"
+        "- Keep responses concise but thorough."
+    )
+
+    config = {"configurable": {"thread_id": thread_id, "provider": "groq"}}
+    await chat_app.ainvoke(
+        {"messages": [SystemMessage(content=system_content)]}, config=config
+    )
+    logger.info(f"Chat thread {thread_id} initialised with article context.")
+
+
+async def send_chat_message(
+    thread_id: str, message: str, provider: str = "groq"
+) -> str:
+    config = {"configurable": {"thread_id": thread_id, "provider": provider}}
+    result = await chat_app.ainvoke(
+        {"messages": [HumanMessage(content=message)]}, config=config
+    )
+    ai_msg = result["messages"][-1]
+    return ai_msg.content if hasattr(ai_msg, "content") else str(ai_msg)

backend/app/modules/chat/get_rag_data.py

@@ -31,7 +31,7 @@
 
 load_dotenv()
 
-pc = Pinecone(os.getenv("PINECONE_API_KEY"))
+pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
 index = pc.Index("perspective")
 
 

backend/app/modules/chat/llm_processing.py

@@ -27,6 +27,7 @@
 from groq import Groq
 from dotenv import load_dotenv
 from app.logging.logging_config import setup_logger
+from app.llm_config import LLM_MODEL
 
 logger = setup_logger(__name__)
 
@@ -55,7 +56,7 @@ def ask_llm(question, docs):
 """
 
     response = client.chat.completions.create(
-        model="gemma2-9b-it",
+        model=LLM_MODEL,
         messages=[
             {"role": "system", "content": "Use only the context to answer."},
             {"role": "user", "content": prompt},