Improve Article Tab Layout & Make AI Chat Use Article Context

backend/app/logging/logging_config.py

@@ -24,14 +24,20 @@ def setup_logger(name: str) -> logging.Logger:
         datefmt="%Y-%m-%d %H:%M:%S"
     )
 
-    # Console Handler
+    # Console Handler with UTF-8 encoding for Windows support
     console_handler = logging.StreamHandler(sys.stdout)
     console_handler.setLevel(logging.INFO)
     console_handler.setFormatter(formatter)
+    # Enable UTF-8 encoding to handle emoji and special characters on Windows
+    if hasattr(console_handler.stream, 'reconfigure'):
+        try:
+            console_handler.stream.reconfigure(encoding='utf-8')
+        except (AttributeError, ValueError):
+            pass
     logger.addHandler(console_handler)
 
-    # File Handler
-    file_handler = logging.FileHandler("app.log")
+    # File Handler with UTF-8 encoding
+    file_handler = logging.FileHandler("app.log", encoding="utf-8")
     file_handler.setLevel(logging.DEBUG)  # Keep detailed logs in file
     file_handler.setFormatter(formatter)
     logger.addHandler(file_handler)

backend/app/modules/bias_detection/check_bias.py

@@ -61,7 +61,7 @@ def check_bias(text):
                     "content": (f"Give bias score to the following article \n\n{text}"),
                 },
             ],
-            model="gemma2-9b-it",
+            model="llama-3.1-8b-instant",
             temperature=0.3,
             max_tokens=512,
         )

backend/app/modules/chat/llm_processing.py

@@ -39,11 +39,23 @@ def build_context(docs):
     return "\n".join(
         f"{m['metadata'].get('explanation') or m['metadata'].get('reasoning', '')}"
         for m in docs
-    )
+    ).strip()
+
+
+def ask_llm(question, docs, article_context=""):
+    rag_context = build_context(docs)
+    article_context = (article_context or "").strip()
+
+    if rag_context and article_context:
+        context = f"Article:\n{article_context}\n\nRetrieved Insights:\n{rag_context}"
+    else:
+        context = rag_context or article_context
 
+    if not context:
+        return (
+            "I don't have article context yet. Please analyze an article first and then ask me again."
+        )
 
-def ask_llm(question, docs):
-    context = build_context(docs)
     logger.debug(f"Generated context for LLM:\n{context}")
     prompt = f"""You are an assistant that answers based on context.
 
@@ -55,11 +67,13 @@ def ask_llm(question, docs):
 """
 
     response = client.chat.completions.create(
-        model="gemma2-9b-it",
+        model="llama-3.1-8b-instant",
         messages=[
             {"role": "system", "content": "Use only the context to answer."},
             {"role": "user", "content": prompt},
         ],
     )
     logger.info("LLM response retrieved successfully.")
     return response.choices[0].message.content
+
+

backend/app/modules/facts_check/llm_processing.py

@@ -63,7 +63,7 @@ def run_claim_extractor_sdk(state):
                     ),
                 },
             ],
-            model="gemma2-9b-it",
+            model="llama-3.1-8b-instant",
             temperature=0.3,
             max_tokens=512,
         )
@@ -128,7 +128,7 @@ def run_fact_verifier_sdk(search_results):
                         ),
                     },
                 ],
-                model="gemma2-9b-it",
+                model="llama-3.1-8b-instant",
                 temperature=0.3,
                 max_tokens=256,
             )

backend/app/modules/facts_check/web_search.py

@@ -28,15 +28,24 @@
 
 
 def search_google(query):
-    results = requests.get(
-        f"https://www.googleapis.com/customsearch/v1?key={GOOGLE_SEARCH}&cx=f637ab77b5d8b4a3c&q={query}"
-    )
-    res = results.json()
-    first = {}
-    first["title"] = res["items"][0]["title"]
-    first["link"] = res["items"][0]["link"]
-    first["snippet"] = res["items"][0]["snippet"]
-
-    return [
-        first,
-    ]
+    try:
+        results = requests.get(
+            f"https://www.googleapis.com/customsearch/v1?key={GOOGLE_SEARCH}&cx=f637ab77b5d8b4a3c&q={query}"
+        )
+        res = results.json()
+
+        # Check if the response contains 'items' (successful search)
+        if "items" not in res:
+            # Handle error responses from Google API
+            error_msg = res.get("error", {}).get("message", "Unknown error")
+            raise ValueError(f"Google API Error: {error_msg}")
+
+        first = {}
+        first["title"] = res["items"][0]["title"]
+        first["link"] = res["items"][0]["link"]
+        first["snippet"] = res["items"][0]["snippet"]
+
+        return [first]
+    except Exception as e:
+        print(f"Search Google Error: {e}")
+        raise

backend/app/modules/langgraph_nodes/judge.py

@@ -24,7 +24,7 @@
 
 # Init once
 groq_llm = ChatGroq(
-    model="gemma2-9b-it",
+    model="llama-3.1-8b-instant",
     temperature=0.0,
     max_tokens=10,
 )

backend/app/modules/langgraph_nodes/sentiment.py

@@ -49,7 +49,7 @@ def run_sentiment_sdk(state):
                     ),
                 },
             ],
-            model="gemma2-9b-it",
+            model="llama-3.1-8b-instant",
             temperature=0.2,
             max_tokens=3,
         )

backend/app/routes/routes.py

@@ -30,7 +30,7 @@
 """
 
 
-from fastapi import APIRouter
+from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
 from app.modules.pipeline import run_scraper_pipeline
 from app.modules.pipeline import run_langgraph_workflow
@@ -52,6 +52,7 @@ class URlRequest(BaseModel):
 
 class ChatQuery(BaseModel):
     message: str
+    article_context: str | None = None
 
 
 @router.get("/")
@@ -77,9 +78,20 @@ async def run_pipelines(request: URlRequest):
 
 @router.post("/chat")
 async def answer_query(request: ChatQuery):
-    query = request.message
-    results = search_pinecone(query)
-    answer = ask_llm(query, results)
-    logger.info(f"Chat answer generated: {answer}")
-
-    return {"answer": answer}
+    try:
+        query = request.message.strip()
+        if not query:
+            raise HTTPException(status_code=400, detail="Message cannot be empty.")
+
+        article_context = (request.article_context or "").strip()
+
+        results = search_pinecone(query)
+        answer = ask_llm(query, results, article_context)
+        logger.info("Chat answer generated successfully.")
+
+        return {"answer": answer}
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.exception(f"Chat request failed: {e}")
+        raise HTTPException(status_code=500, detail="Failed to generate chat response.")

backend/app/utils/fact_check_utils.py

@@ -45,36 +45,39 @@ def run_fact_check_pipeline(state):
     result = run_claim_extractor_sdk(state)
 
     if state.get("status") != "success":
-        logger.error("❌ Claim extraction failed.")
+        logger.error("Claim extraction failed.")
         return [], "Claim extraction failed."
 
     # Step 1: Extract claims
     raw_output = result.get("verifiable_claims", "")
     claims = re.findall(r"^[\*\-•]\s+(.*)", raw_output, re.MULTILINE)
     claims = [claim.strip() for claim in claims if claim.strip()]
-    logger.info(f"🧠 Extracted claims: {claims}")
+    logger.info(f"Extracted claims: {claims}")
 
     if not claims:
         return [], "No verifiable claims found."
 
     # Step 2: Search each claim with polite delay
     search_results = []
     for claim in claims:
-        logger.info(f"\n🔍 Searching for claim: {claim}")
+        logger.info(f"Searching for claim: {claim}")
         try:
             results = search_google(claim)
             if results:
                 results[0]["claim"] = claim
                 search_results.append(results[0])
-                logger.info(f"✅ Found result: {results[0]['title']}")
+                logger.info(f"Found result: {results[0]['title']}")
             else:
-                logger.warning(f"⚠️ No search result for: {claim}")
+                logger.warning(f"No search result for: {claim}")
         except Exception as e:
-            logger.error(f"❌ Search failed for: {claim} -> {e}")
+            logger.error(f"Search failed for: {claim} -> {e}")
 
     if not search_results:
+        logger.error("All claim searches failed or returned no results.")
         return [], "All claim searches failed or returned no results."
 
     # Step 3: Verify facts using LLM
+    logger.info(f"Verifying {len(search_results)} claims using LLM...")
     final = run_fact_verifier_sdk(search_results)
+    logger.info("Fact-checking pipeline completed successfully.")
     return final.get("verifications", []), None

frontend/app/analyze/loading/page.tsx

@@ -16,7 +16,7 @@ import {
 import ThemeToggle from "@/components/theme-toggle";
 import axios from "axios";
 
-// const backend_url = process.env.NEXT_PUBLIC_API_URL;
+const backend_url = process.env.NEXT_PUBLIC_API_URL;
 
 
 
@@ -74,10 +74,10 @@ export default function LoadingPage() {
 
         try {
           const [processRes, biasRes] = await Promise.all([
-            axios.post("https://thunder1245-perspective-backend.hf.space/api/process", {
+            axios.post(`${backend_url}/api/process`, {
               url: storedUrl,
             }),
-            axios.post("https://thunder1245-perspective-backend.hf.space/api/bias", {
+            axios.post(`${backend_url}/api/bias`, {
               url: storedUrl,
             }),
           ]);