Update app.py

app.py

@@ -5,27 +5,27 @@ import gradio as gr
 API_URL = "https://api-inference.huggingface.co/models/ProsusAI/finbert"
 headers = {"Authorization": "Bearer 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"}
 
-# def query(payload):
-# 	response = requests.post(API_URL, headers=headers, json=payload)
-# 	return response.json()[0]
+def query(payload):
+	response = requests.post(API_URL, headers=headers, text=payload)
+	return response.text()
 	
 output = query({
 	"inputs": "I like you. I love you",
 })
 
 
-def predict_sentiment(payload):
-    # Sentiment Analysis
-    # sentiment_inputs = sentiment_tokenizer(headline, padding=True, truncation=True, return_tensors='pt')
-    with torch.no_grad():
-        sentiment_outputs = requests.post(API_URL, headers=headers, json=payload)
-        sentiment_prediction = torch.nn.functional.softmax(sentiment_outputs.logits, dim=-1)
+# def predict_sentiment(payload):
+#     # Sentiment Analysis
+    
+#     with torch.no_grad():
+#         sentiment_outputs = requests.post(API_URL, headers=headers, json=payload)
+#         sentiment_prediction = torch.nn.functional.softmax(sentiment_outputs.logits, dim=-1)
 
-    pos, neg, neutr = sentiment_prediction[:, 0].item(), sentiment_prediction[:, 1].item(), sentiment_prediction[:, 2].item()
-    sentiment_label = "Positive" if pos > neg and pos > neutr else "Negative" if neg > pos and neg > neutr else "Neutral"
+#     pos, neg, neutr = sentiment_prediction[:, 0].item(), sentiment_prediction[:, 1].item(), sentiment_prediction[:, 2].item()
+#     sentiment_label = "Positive" if pos > neg and pos > neutr else "Negative" if neg > pos and neg > neutr else "Neutral"
 
 
-    return sentiment_label
+#     return sentiment_label