Added journal page functionality

app.py

@@ -1,11 +1,9 @@
 from flask import Flask, request, jsonify
 from huggingface_hub import InferenceClient
 
-# Initialize Flask app and Hugging Face client
 app = Flask(__name__)
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# Helper function to generate a response from the AI model
 def generate_response(message, history, system_message, max_tokens, temperature, top_p):
     messages = [{"role": "system", "content": system_message}]
 
@@ -18,7 +16,6 @@ def generate_response(message, history, system_message, max_tokens, temperature,
     messages.append({"role": "user", "content": message})
     response = ""
 
-    # Streaming response from the Hugging Face model
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -31,10 +28,29 @@ def generate_response(message, history, system_message, max_tokens, temperature,
 
     return response
 
+def generate_journal_suggestion(current_page):
+    suggestion_prompt = (
+        f"Pe baza înregistrării din jurnal: '{current_page}', sugerează ce ar putea scrie utilizatorul în continuare în jurnalul său. "
+        "Concentrează-te pe încurajarea reflecției mai profunde, stabilirea obiectivelor sau explorarea ideilor conexe."
+    )
+    suggestion_response = ""
+
+    for message in client.chat_completion(
+        [{"role": "user", "content": suggestion_prompt}],
+        max_tokens=150,
+        stream=True,
+        temperature=0.7,
+        top_p=0.9,
+    ):
+        token = message.choices[0].delta.content
+        suggestion_response += token
+
+    return suggestion_response
+
 @app.route("/", methods=["POST","GET"])
 def home():
     return "Hi!"
-# API endpoint to handle requests
+
 @app.route("/chat", methods=["POST"])
 def chat():
     try:
@@ -45,15 +61,18 @@ def chat():
         max_tokens = data.get("max_tokens", 512)
         temperature = data.get("temperature", 0.7)
         top_p = data.get("top_p", 0.95)
+        journal_page = data.get("journal_page", "")
 
-        # Validate inputs
         if not isinstance(history, list) or not all(isinstance(pair, list) for pair in history):
             return jsonify({"error": "Invalid history format. It should be a list of [message, response] pairs."}), 400
 
-        # Generate AI response
         response = generate_response(message, history, system_message, max_tokens, temperature, top_p)
 
-        return jsonify({"response": response})
+        suggestion = ""
+        if journal_page:
+            suggestion = generate_journal_suggestion(journal_page)
+
+        return jsonify({"response": response, "journal_suggestion": suggestion})
     except Exception as e:
         return jsonify({"error": str(e)}), 500