Update app.py

app.py

@@ -1,3 +1,4 @@
+from AICodeInit import *
 from peft import AutoPeftModelForCausalLM
 from transformers import AutoTokenizer, pipeline
 import gradio as gr
@@ -13,39 +14,64 @@ if api_key is None:
 login(api_key)
 
 # Load the model
-model = AutoPeftModelForCausalLM.from_pretrained("Moritz-Pfeifer/financial-times-classification-llama-2-7b-v1.3")
-tokenizer = AutoTokenizer.from_pretrained("Moritz-Pfeifer/financial-times-classification-llama-2-7b-v1.3")
-
-def predict_text(test, model, tokenizer):
-    prompt = f"""
-            You are given a news article regarding the greater Boston area.
-            Analyze the sentiment of the article enclosed in square brackets,
-            determine if it is positive, negative or other, and return the answer as the corresponding sentiment label
-            "positive" or "negative". If the sentiment is neither positive or negative, return "other".
-
-            [{test}] ="""
-    pipe = pipeline(task="text-generation",
-                        model=model,
-                        tokenizer=tokenizer,
-                        max_new_tokens = 1,
-                        temperature = 0.1,
-                       )
-    result = pipe(prompt)
-    answer = result[0]['generated_text'].split("=")[-1]
-    # print(answer)
-    if "positive" in answer.lower():
-        return "positive"
-    elif "negative" in answer.lower():
-        return "negative"
-    else:
-        return "other"
-
-def predict(input_text):
-  return predict_text(input_text, model, tokenizer)
-
-
-interface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Text Classifier", description="Insert your text and get the classification result.")
-interface.launch()
+# model = AutoPeftModelForCausalLM.from_pretrained("Moritz-Pfeifer/financial-times-classification-llama-2-7b-v1.3")
+# tokenizer = AutoTokenizer.from_pretrained("Moritz-Pfeifer/financial-times-classification-llama-2-7b-v1.3")
+
+# def predict_text(test, model, tokenizer):
+#     prompt = f"""
+#             You are given a news article regarding the greater Boston area.
+#             Analyze the sentiment of the article enclosed in square brackets,
+#             determine if it is positive, negative or other, and return the answer as the corresponding sentiment label
+#             "positive" or "negative". If the sentiment is neither positive or negative, return "other".
+
+#             [{test}] ="""
+#     pipe = pipeline(task="text-generation",
+#                         model=model,
+#                         tokenizer=tokenizer,
+#                         max_new_tokens = 1,
+#                         temperature = 0.1,
+#                        )
+#     result = pipe(prompt)
+#     answer = result[0]['generated_text'].split("=")[-1]
+#     # print(answer)
+#     if "positive" in answer.lower():
+#         return "positive"
+#     elif "negative" in answer.lower():
+#         return "negative"
+#     else:
+#         return "other"
+
+# def predict(input_text):
+#   return predict_text(input_text, model, tokenizer)
+
+
+# interface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Text Classifier", description="Insert your text and get the classification result.")
+# interface.launch()
+
+#initialize llama model model, tokenizer, prompt, pipe = create_pipeline()
+nlp = load_model()
+prompt, pipe = create_pipeline()
+#demo function calls predict text from other file
+def entity_sentiment(text):
+    entities = extract_entities(text,nlp)
+    entities_context = extract_entities_with_context(text, nlp)
+    #sentiment = get_sentiment(text)
+    entity_sentiment_scoreonly=analyze_entity_sentiments_score(entities_context)
+    #sentiment_category = categorize_sentiment(sentiment)
+    #average_sentiment = sum(entity_sentiments)/len(entity_sentiments)
+    average_score = calculate_avg_score(entity_sentiment_scoreonly)
+    average_sentiment = categorize_sentiment(average_score)
+
+    llama_sentiment = predict_text(text,pipe,prompt)
+    return entities,entities_context,entity_sentiment_scoreonly,average_score,average_sentiment,llama_sentiment
+
+demo = gr.Interface(
+    fn=entity_sentiment,
+    inputs=["text"],
+    outputs=["text","text","text","text","text"],
+)
+
+demo.launch(share=True)
 
 if __name__ == "__main__":
     interface.launch(share=True)