Update app.py

app.py

@@ -4,6 +4,11 @@ import tensorflow as tf
 import json
 import os
 
+data = {"Interactions":[]}
+
+with open("question_answer.json", "w") as file:
+    json.dump(data, file, indent=4)
+        
 print("Loading the model......")
 model_name = "WICKED4950/Irisonego5"
 strategy = tf.distribute.MirroredStrategy()
@@ -13,35 +18,36 @@ with strategy.scope():
     model = TFBlenderbotForConditionalGeneration.from_pretrained(model_name)
 
 def save_question(question,answer,path = "question_answer.json"):
-    print(f"Saving data to: {os.path.abspath(path)}")
     with open(path, "r") as file:
         data = json.load(file)
     data["Interactions"].append({"Question:":question,"Answer:":answer})
     print(data)
     with open(path, "w") as file:
         json.dump(data, file, indent=4)
-    print("saving question")
         
 print("Interface getting done....")
 # Define the chatbot function
 def predict(user_input):
-    # Tokenize input text
-    inputs = tokenizer(user_input, return_tensors="tf", padding=True, truncation=True)
-
-    # Generate the response using the model
-    response_id = model.generate(
-        inputs['input_ids'],
-        max_length=128,         # Set max length of response
-        do_sample=True,         # Sampling for variability
-        top_k=15,               # Consider top 50 tokens
-        top_p=0.95,             # Nucleus sampling
-        temperature=0.8         # Adjusts creativity of response
-    )
-
-    # Decode the response
-    response = tokenizer.decode(response_id[0], skip_special_tokens=True)
-    save_question(question = user_input,answer=response)
-    return response
+    if user_input == "Print_data_hmm":
+        print(json.dumps(data, indent=4))
+        return "Done"
+    else:
+        inputs = tokenizer(user_input, return_tensors="tf", padding=True, truncation=True)
+    
+        # Generate the response using the model
+        response_id = model.generate(
+            inputs['input_ids'],
+            max_length=128,         # Set max length of response
+            do_sample=True,         # Sampling for variability
+            top_k=15,               # Consider top 50 tokens
+            top_p=0.95,             # Nucleus sampling
+            temperature=0.8         # Adjusts creativity of response
+        )
+    
+        # Decode the response
+        response = tokenizer.decode(response_id[0], skip_special_tokens=True)
+        save_question(question = user_input,answer=response)
+        return response
 
 # Gradio interface
 gr.Interface(