Update app.py

app.py

@@ -1,35 +1,26 @@
-import os
-import zipfile
 import pandas as pd
 import gradio as gr
 from sentence_transformers import SentenceTransformer
 import faiss
 import numpy as np
 
-# Step 1: Unzip the dataset
-zip_file = "climate.zip"  # your uploaded zip file
-extracted_path = "climate_data"
-
-if not os.path.exists(extracted_path):
-    with zipfile.ZipFile(zip_file, 'r') as zip_ref:
-        zip_ref.extractall(extracted_path)
-
-# Step 2: Load train.csv
-csv_path = os.path.join(extracted_path, "train.csv")
+# Step 1: Load train.csv directly
+csv_path = "train.csv"  # file you uploaded directly
 df = pd.read_csv(csv_path, header=None, names=["label", "title", "description"])
 
-# Combine title and description for semantic search
+# Step 2: Combine title and description
 df["content"] = df["title"].fillna("") + ". " + df["description"].fillna("")
 
-# Step 3: Encode using SentenceTransformer
+# Step 3: Encode content using SentenceTransformer
 model = SentenceTransformer('all-MiniLM-L6-v2')
 corpus_embeddings = model.encode(df["content"].tolist(), show_progress_bar=True)
 
-# Step 4: Create FAISS index
+# Step 4: Build FAISS index
 embedding_dim = corpus_embeddings.shape[1]
 index = faiss.IndexFlatL2(embedding_dim)
 index.add(corpus_embeddings)
 
+# Step 5: Define retrieval function
 def retrieve_and_respond(claim, k=5):
     query_embedding = model.encode([claim])
     D, I = index.search(np.array(query_embedding), k)
@@ -41,13 +32,13 @@ def retrieve_and_respond(claim, k=5):
     
     return "\n\n".join(results)
 
-# Step 5: Gradio Interface
+# Step 6: Gradio UI
 iface = gr.Interface(
     fn=retrieve_and_respond,
     inputs=gr.Textbox(lines=2, placeholder="Enter a news-related claim here..."),
     outputs="markdown",
-    title="Claim Verifier using RAG (AG News)",
-    description="Enter a claim and retrieve the most relevant AG News articles to verify or refute it."
+    title="Claim Verifier using AG News",
+    description="Enter a claim and get the most relevant AG News articles to help verify or refute it."
 )
 
 iface.launch()