app changes

app.py

@@ -28,50 +28,6 @@ llm = HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
 
 class Sentiment(BaseModel):
     label: str = Field(description="Is a the above rview sentiment 'Good', or 'Bad' ?")
-
-
-
-
-def sentence_builder(Model,Text):
-    if Model=="Sentiment analysis pipeline":
-        good_label,bad_label=pipeline_sentiment(Text)
-    if Model=="Falcon-7b-instruct":
-        good_label,bad_label=falcon_sentiment(Text)
-    if Model=="GPT-4 Function call":
-        good_label,bad_label=gpt4_sentiment(Text)
-        
-    print({"Good": good_label, "Bad": bad_label})
-    return {"Good": good_label, "Bad": bad_label}
-
-
-demo = gr.Interface(
-    sentence_builder,
-    [
-     
-        gr.Dropdown(
-            ["Sentiment analysis pipeline","Falcon-7b-instruct","GPT-4 Function call"], label="Model", info="Wich model to use"
-        ),
-        gr.Textbox(
-            label="Text",
-            info="Review text",
-            lines=2,
-            value="I'm not sure about the origin of this product, it seems suspicious.",
-        ),
-        
-    ],
-   "label",
-    examples=[
-        
-        ["Sentiment analysis pipeline","The product broke ! Great ..."],
-        ["Sentiment analysis pipeline","Not sure if I like it or not."],
-        ["Sentiment analysis pipeline","This product is just a toy."],
-        ["Sentiment analysis pipeline","Bought a TV, received an Ipad..."],
-        ["Sentiment analysis pipeline","Could have found the same on wish.com ."],
-        ["Sentiment analysis pipeline","They did a wonderfull job at ripping us."],
-        ["Sentiment analysis pipeline","Is it dropshipping ?"],
-    ]
-)
-
 def pipeline_sentiment(text):
     out = classifier(text)
     print(out)
@@ -154,6 +110,48 @@ def Find_sentiment(sentence):
 def gpt4_sentiment(text):
     out=Find_sentiment(text)
     return out=="Good",out=='Bad'
+
+
+
+def sentence_builder(Model,Text):
+    if Model=="Sentiment analysis pipeline":
+        good_label,bad_label=pipeline_sentiment(Text)
+    if Model=="Falcon-7b-instruct":
+        good_label,bad_label=falcon_sentiment(Text)
+    if Model=="GPT-4 Function call":
+        good_label,bad_label=gpt4_sentiment(Text)
+        
+    print({"Good": good_label, "Bad": bad_label})
+    return {"Good": good_label, "Bad": bad_label}
+
+
+demo = gr.Interface(
+    sentence_builder,
+    [
+     
+        gr.Dropdown(
+            ["Sentiment analysis pipeline","Falcon-7b-instruct","GPT-4 Function call"], label="Model", info="Wich model to use"
+        ),
+        gr.Textbox(
+            label="Text",
+            info="Review text",
+            lines=2,
+            value="I'm not sure about the origin of this product, it seems suspicious.",
+        ),
+        
+    ],
+   "label",
+    examples=[
+        
+        ["Sentiment analysis pipeline","The product broke ! Great ..."],
+        ["Sentiment analysis pipeline","Not sure if I like it or not."],
+        ["Sentiment analysis pipeline","This product is just a toy."],
+        ["Sentiment analysis pipeline","Bought a TV, received an Ipad..."],
+        ["Sentiment analysis pipeline","Could have found the same on wish.com ."],
+        ["Sentiment analysis pipeline","They did a wonderfull job at ripping us."],
+        ["Sentiment analysis pipeline","Is it dropshipping ?"],
+    ]
+)
 if __name__ == "__main__":
     
     demo.launch()