Update README.md

README.md

@@ -60,27 +60,34 @@ model = PaliGemmaForConditionalGeneration.from_pretrained(
 processor = AutoProcessor.from_pretrained(model_id)processor = AutoProcessor.from_pretrained(model_id)
 
 ### Model inputs and ouputs
-headine="headline of the news article"
-article="News body text"
-img_path="Path of the image"
-image=Image.open(img_path).convert('RGB')
-prompt=f"""You are a news classifer AI assitant. You are given with a news article that contains headline, body text and image.
-            Your task is to analyze the headline, body text and image, and classify the news as biased or unbiased. 
-            
-            In this particular task, the term 'biased' represents disinformation, propaganda, loaded language, negative associations, generalization, harm, hatred, satire\
-            whereas 'unbiased' represents real news without the spread of misinformation, disinformation, and propaganda.\
-            
-            headlines: {headline}
-            news body text: {article}. 
-            What would be the label for this news article considering features from both texts and image?
-            """
-
-model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
-input_len = model_inputs["input_ids"].shape[-1]
-generation = model.generate(**model_inputs, max_new_tokens=20, do_sample=False)
-generation = generation[0][input_len:]
-label = processor.decode(generation, skip_special_tokens=True)
-print(label)
+img_path="Enter your image file driectory"
+text_path="Enter article data path like csv file with image file names and corresponding news articles"
+df = pd.read_csv(text_path)
+
+for idx, row in test_df.iterrows():
+  img=row['image_filename'] # image file names in the csv file
+
+  headine=row['Headline'] # take the headline columns from the csv files.
+  article=row['article'] # article names
+  img_path=img_dir+f"/{img}" #full path of the image
+  image=Image.open(img_path).convert('RGB')
+  prompt=f"""You are a news classifer AI assitant. You are given with a news article that contains headline, body text and image.
+              Your task is to analyze the headline, body text and image, and classify the news as biased or unbiased. 
+              
+              In this particular task, the term 'biased' represents disinformation, propaganda, loaded language, negative associations, generalization, harm, hatred, satire\
+              whereas 'unbiased' represents real news without the spread of misinformation, disinformation, and propaganda.\
+              
+              headlines: {headline}
+              news body text: {article}. 
+              What would be the label for this news article considering features from both texts and image?
+              """
+  
+  model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)
+  input_len = model_inputs["input_ids"].shape[-1]
+  generation = model.generate(**model_inputs, max_new_tokens=20, do_sample=False)
+  generation = generation[0][input_len:]
+  label = processor.decode(generation, skip_special_tokens=True)
+  print(label)
 
 ###