optimizations and formatting frontend and backend

README.md

@@ -134,6 +134,15 @@ sequenceDiagram
 ## Setup & Deployment
 
 ### Frontend Setup
+
+Setup environment variables:*
+  - add .env file in `/frontend`directory.
+  - add following environment variable in your .env file.
+```
+NEXT_PUBLIC_API_URL = http://localhost:8000
+
+```
+
 ```bash
 cd frontend
 npm install
@@ -151,10 +160,10 @@ npm run dev
 
 
 *Setup environment variables:*
-  - add .env file in `/new-backend`directory.
+  - add .env file in `/backend`directory.
   - add following environment variable in your .env file.
   ```
-  GROQ_API_KEY= <groq_api_key>
+GROQ_API_KEY= <groq_api_key>
 PINECONE_API_KEY = <your_pinecone_API_KEY>
 PORT = 8000
 SEARCH_KEY = <your_Google_custom_search_engine_API_key>

backend/app/db/vector_store.py

@@ -6,7 +6,6 @@
 if not PINECONE_API_KEY:
     raise ValueError("PINECONE_API_KEY environment variable is required")
 try:
-
     # Initialize Pinecone client
     pc = Pinecone(api_key=PINECONE_API_KEY)
 
@@ -25,10 +24,7 @@
         name=INDEX_NAME,
         dimension=DIMENSIONS,
         metric=METRIC,
-        spec=ServerlessSpec(
-            cloud=CloudProvider.AWS,
-            region=AwsRegion.US_EAST_1
-        )
+        spec=ServerlessSpec(cloud=CloudProvider.AWS, region=AwsRegion.US_EAST_1),
     )
 else:
     print(f"Index '{INDEX_NAME}' already exists")
@@ -37,5 +33,4 @@
     # Connect to the index
     index = pc.Index(INDEX_NAME)
 except Exception as e:
-    raise RuntimeError("Error occured while "
-                       f"connecting to the index {INDEX_NAME}:{e}")
+    raise RuntimeError(f"Error occured while connecting to the index {INDEX_NAME}:{e}")

backend/app/modules/bias_detection/check_bias.py

@@ -12,7 +12,7 @@ def check_bias(text):
     try:
         print(text)
         print(json.dumps(text))
-        
+
         if not text:
             raise ValueError("Missing or empty 'cleaned_text'")
 
@@ -30,10 +30,7 @@ def check_bias(text):
                 },
                 {
                     "role": "user",
-                    "content": (
-                        "Give bias score to the following article "
-                        f"\n\n{text}"
-                    ),
+                    "content": (f"Give bias score to the following article \n\n{text}"),
                 },
             ],
             model="gemma2-9b-it",

backend/app/modules/chat/embed_query.py

@@ -4,7 +4,6 @@
 
 
 def embed_query(query: str):
-
     embeddings = embedder.encode(query).tolist()
 
     return embeddings

backend/app/modules/chat/get_rag_data.py

@@ -10,22 +10,15 @@
 
 
 def search_pinecone(query: str, top_k: int = 5):
-
     embeddings = embed_query(query)
 
     results = index.query(
-        vector=embeddings,
-        top_k=top_k,
-        include_metadata=True,
-        namespace="default"
-
+        vector=embeddings, top_k=top_k, include_metadata=True, namespace="default"
     )
 
     matches = []
     for match in results["matches"]:
-        matches.append({
-            "id": match["id"],
-            "score": match["score"],
-            "metadata": match["metadata"]
-        })
+        matches.append(
+            {"id": match["id"], "score": match["score"], "metadata": match["metadata"]}
+        )
     return matches

backend/app/modules/chat/llm_processing.py

@@ -8,8 +8,10 @@
 
 
 def build_context(docs):
-
-    return "\n".join(f"{m['metadata'].get('explanation') or m['metadata'].get('reasoning', '')}"for m in docs)
+    return "\n".join(
+        f"{m['metadata'].get('explanation') or m['metadata'].get('reasoning', '')}"
+        for m in docs
+    )
 
 
 def ask_llm(question, docs):
@@ -28,8 +30,8 @@ def ask_llm(question, docs):
         model="gemma2-9b-it",
         messages=[
             {"role": "system", "content": "Use only the context to answer."},
-            {"role": "user", "content": prompt}
-        ]
+            {"role": "user", "content": prompt},
+        ],
     )
 
     return response.choices[0].message.content

backend/app/modules/facts_check/web_search.py

@@ -6,14 +6,17 @@
 
 GOOGLE_SEARCH = os.getenv("SEARCH_KEY")
 
+
 def search_google(query):
-    results = requests.get(f"https://www.googleapis.com/customsearch/v1?key={GOOGLE_SEARCH}&cx=f637ab77b5d8b4a3c&q={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,
-    ]
+    ]

backend/app/modules/langgraph_builder.py

@@ -5,8 +5,8 @@
     generate_perspective,
     judge,
     store_and_send,
-    error_handler
-    )
+    error_handler,
+)
 
 from typing_extensions import TypedDict
 
@@ -24,58 +24,34 @@ class MyState(TypedDict):
 def build_langgraph():
     graph = StateGraph(MyState)
 
-    graph.add_node(
-                    "sentiment_analysis",
-                    sentiment.run_sentiment_sdk
-                    )
-    graph.add_node(
-                    "fact_checking",
-                    fact_check.run_fact_check
-                    )
-    graph.add_node(
-                    "generate_perspective",
-                    generate_perspective.generate_perspective
-                    )
-    graph.add_node(
-                    "judge_perspective",
-                    judge.judge_perspective
-                    )
-    graph.add_node(
-                    "store_and_send",
-                    store_and_send.store_and_send
-                    )
-    graph.add_node(
-                    "error_handler",
-                    error_handler.error_handler
-                    )
+    graph.add_node("sentiment_analysis", sentiment.run_sentiment_sdk)
+    graph.add_node("fact_checking", fact_check.run_fact_check)
+    graph.add_node("generate_perspective", generate_perspective.generate_perspective)
+    graph.add_node("judge_perspective", judge.judge_perspective)
+    graph.add_node("store_and_send", store_and_send.store_and_send)
+    graph.add_node("error_handler", error_handler.error_handler)
 
     graph.set_entry_point(
-                    "sentiment_analysis",
-                    )
+        "sentiment_analysis",
+    )
 
     graph.add_conditional_edges(
         "sentiment_analysis",
-        lambda x: (
-            "error_handler" if x.get("status") == "error" else "fact_checking"
-            )
+        lambda x: ("error_handler" if x.get("status") == "error" else "fact_checking"),
     )
 
     graph.add_conditional_edges(
         "fact_checking",
         lambda x: (
-            "error_handler"
-            if x.get("status") == "error"
-            else "generate_perspective"
-            )
+            "error_handler" if x.get("status") == "error" else "generate_perspective"
+        ),
     )
 
     graph.add_conditional_edges(
         "generate_perspective",
         lambda x: (
-            "error_handler"
-            if x.get("status") == "error"
-            else "judge_perspective"
-            )
+            "error_handler" if x.get("status") == "error" else "judge_perspective"
+        ),
     )
 
     graph.add_conditional_edges(
@@ -90,15 +66,11 @@ def build_langgraph():
             )
             if state.get("score", 0) < 70
             else "store_and_send"
-            )
+        ),
     )
     graph.add_conditional_edges(
         "store_and_send",
-        lambda x: (
-            "error_handler"
-            if x.get("status") == "error"
-            else "__end__"
-            )
+        lambda x: ("error_handler" if x.get("status") == "error" else "__end__"),
     )
 
     graph.set_finish_point("store_and_send")

backend/app/modules/langgraph_nodes/error_handler.py

@@ -1,11 +1,10 @@
-
-
 def error_handler(input):
     print("Error detected!")
     print(f"From: {input.get('error_from')}")
     print(f"Message: {input.get('message')}")
 
-    return {"status": "stopped_due_to_error",
-            "from": [input.get("error_from")],
-            "error": [input.get("message")]
-            }
+    return {
+        "status": "stopped_due_to_error",
+        "from": [input.get("error_from")],
+        "error": [input.get("message")],
+    }

backend/app/modules/langgraph_nodes/fact_check.py

@@ -1,4 +1,3 @@
-
 from app.utils.fact_check_utils import run_fact_check_pipeline
 
 
@@ -25,9 +24,5 @@ def run_fact_check(state):
             "status": "error",
             "error_from": "fact_checking",
             "message": f"{e}",
-            }
-    return {
-        **state,
-        "facts": verifications,
-        "status": "success"
         }
+    return {**state, "facts": verifications, "status": "success"}

backend/app/modules/langgraph_nodes/generate_perspective.py

@@ -13,10 +13,7 @@ class PerspectiveOutput(BaseModel):
 
 my_llm = "llama-3.3-70b-versatile"
 
-llm = ChatGroq(
-    model=my_llm,
-    temperature=0.7
-)
+llm = ChatGroq(model=my_llm, temperature=0.7)
 
 structured_llm = llm.with_structured_output(PerspectiveOutput)
 
@@ -37,24 +34,27 @@ def generate_perspective(state):
         elif not facts:
             raise ValueError("Missing or empty 'facts' in state")
 
-        facts_str = "\n".join([f"Claim: {f['original_claim']}\n"
-                               "Verdict: {f['verdict']}\nExplanation: "
-                               "{f['explanation']}" for f in state["facts"]])
-
-        result = chain.invoke({
-            "cleaned_article": text,
-            "facts": facts_str,
-            "sentiment": state.get("sentiment", "neutral")
-        })
+        facts_str = "\n".join(
+            [
+                f"Claim: {f['original_claim']}\n"
+                "Verdict: {f['verdict']}\nExplanation: "
+                "{f['explanation']}"
+                for f in state["facts"]
+            ]
+        )
+
+        result = chain.invoke(
+            {
+                "cleaned_article": text,
+                "facts": facts_str,
+                "sentiment": state.get("sentiment", "neutral"),
+            }
+        )
     except Exception as e:
         print(f"some error occured in generate_perspective:{e}")
         return {
             "status": "error",
             "error_from": "generate_perspective",
             "message": f"{e}",
         }
-    return {
-        **state,
-        "perspective": result,
-        "status": "success"
-        }
+    return {**state, "perspective": result, "status": "success"}

backend/app/modules/langgraph_nodes/sentiment.py

@@ -25,9 +25,9 @@ def run_sentiment_sdk(state):
                 },
                 {
                     "role": "user",
-                    "content": ("Analyze the sentiment of the following text:"
-                                f"\n\n{text}"
-                                ),
+                    "content": (
+                        f"Analyze the sentiment of the following text:\n\n{text}"
+                    ),
                 },
             ],
             model="gemma2-9b-it",

backend/app/modules/langgraph_nodes/store_and_send.py

@@ -31,7 +31,4 @@ def store_and_send(state):
             "message": f"{e}",
         }
     #  sending to frontend
-    return {
-        **state,
-        "status": "success"
-        }
+    return {**state, "status": "success"}