Update app.py

app.py

@@ -1,64 +1,65 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, TFBlenderbotForConditionalGeneration
+import tensorflow as tf
+import json
+import os
+from datetime import datetime
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+data = {"Interactions":[]}
+with open("question_answer.json", "w") as file:
+    json.dump(data, file, indent=4)
 
+print("Loading the model......")
+model_name = "WICKED4950/Esther_V1"
+strategy = tf.distribute.MirroredStrategy()
+tf.config.optimizer.set_jit(True)  # Enable XLA
+tokenizer = AutoTokenizer.from_pretrained(model_name) 
+with strategy.scope():
+    model = TFBlenderbotForConditionalGeneration.from_pretrained(model_name)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def save_question(question,answer,path = "question_answer.json"):
+    with open(path, "r") as file:
+        data = json.load(file)
+    data["Interactions"].append({"Question:":question,"Answer:":answer,"Time:":datetime.now().strftime("%Y-%m-%d %H:%M:%S")})
+    with open(path, "w") as file:
+        json.dump(data, file, indent=4)
+        
+print("Interface getting done....")
+# Define the chatbot function
+def predict(user_input):
+    if user_input == "Print_data_hmm":
+        with open("question_answer.json", "r") as file:
+            print(json.load(file))
+        return "Done"
+    else:
+        inputs = tokenizer(user_input, return_tensors="tf", padding=True, truncation=True,max_length=128)
+    
+        # 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=20,               # Consider top 50 tokens
+            top_p=0.90,             # 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
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Gradio interface
+gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Ask Esther anything!"),
+    outputs=gr.Textbox(label="Esther's Response"),
+    examples=[
+        ["What should I do if I'm feeling down?"],
+        ["How do I deal with stress?"],
+        ["Tell me something positive!"]
     ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+     description="A chatbot trained to provide friendly and comforting responses. Type your question below and let Esther help!",
+    title="Esther - Your Friendly Mental Health Chatbot",
+    
+).launch()