Update app.py

app.py

@@ -169,16 +169,27 @@ peft_config = LoraConfig(
 # model = GPT4All(model_path)
 
 def generate_text(prompt):
-#    result = model.generate(prompt)
-#    return result
-     logging.set_verbosity(logging.CRITICAL)
-#    prompt = input()
-     additional_prompt = "You are an AI Medical customer care bot. Please provide detailed and complete answers for only medical questions."
-     prompt = additional_prompt + prompt
-     pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
-     result = pipe(f"<s>[INST] {prompt} [/INST]")
-     output = result[0]['generated_text']
-     return output
+# #    result = model.generate(prompt)
+# #    return result
+#      logging.set_verbosity(logging.CRITICAL)
+# #    prompt = input()
+#      additional_prompt = "You are an AI Medical customer care bot. Please provide detailed and complete answers for only medical questions."
+#      prompt = additional_prompt + prompt
+#      pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+#      result = pipe(f"<s>[INST] {prompt} [/INST]")
+#      output = result[0]['generated_text']
+        # question = row['Question']
+        # print(question)
+        
+        pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200)
+        result = pipe(f"<s>[INST] {prompt} [/INST]")
+        generated_text = result[0]['generated_text']
+        
+        split_text = generated_text.split("[/INST]")
+        generated_content = split_text[1].strip()
+        
+        prediction = generated_content.split("[/]")[0]
+     return prediction
 
 text_generation_interface = gr.Interface(
     fn=generate_text,