try5 app.py

app.py

@@ -7,7 +7,7 @@ memory_db = {}
 # Load the model
 interpreter = pipeline("text2text-generation", model="google/flan-t5-base")
 
-# Few-shot examples (cleaned up to avoid echo loop)
+# Few-shot examples to guide the model
 few_shot_examples = """
 Example 1:
 "sure. whatever you want"
@@ -22,7 +22,7 @@ Example 3:
 → Likely masking frustration or disappointment, doesn’t want to express upset directly.
 """
 
-# Interpret message and return AI explanation + memory log
+# Interpret message and return explanation + memory log
 def interpret_message(msg, name, closeness, traits, relation, past_events, feelings, use_memory):
     if not msg.strip() or not name.strip():
         return "Please enter both a message and a person’s name.", ""
@@ -68,4 +68,83 @@ Infer their hidden intention, emotional state, and subtext. Then suggest how I s
             "interpretation": "TO BE FILLED"
         })
 
-    result = interpreter(prom
+    result = interpreter(prompt, max_new_tokens=80)[0]["generated_text"]
+
+    if use_memory:
+        memory_db[name]["history"][-1]["interpretation"] = result
+
+    memory_log = ""
+    if use_memory and memory_db[name]["history"]:
+        memory_log = "\n\n".join(
+            [f"• \"{entry['message']}\"\n  → {entry['interpretation']}" for entry in memory_db[name]["history"]]
+        )
+
+    return result, memory_log
+
+# Memory Advisor chatbot
+def memory_chat(user_input, person_name):
+    name = person_name.strip().title()
+    if name not in memory_db:
+        return "There's no memory for this person yet. Try interpreting a message first."
+
+    history = memory_db[name]["history"]
+    past_text = "\n\n".join([f"- {h['message']}\n  → {h['interpretation']}" for h in history])
+
+    chat_prompt = f"""You are a relationship and social communication expert trained in human behavior.
+
+Here is what you know about {name}'s recent messages and past behavior:
+{past_text}
+
+The user now asks: {user_input}
+
+Respond with a supportive and emotionally intelligent answer based on past messages.
+Avoid repeating the user's input. Be specific. If {name} is likely upset, say it.
+If the user should give space or check in, say so.
+
+Only output your advice in 2–4 sentences. No explanation of what you’re doing.
+"""
+
+    response = interpreter(chat_prompt, max_new_tokens=80)[0]["generated_text"]
+    return response
+
+# Gradio UI
+with gr.Blocks(title="Textual Chemistry") as app:
+    gr.Markdown("## 💬 Textual Chemistry\n_What do they really mean?_")
+
+    with gr.Row():
+        person_name = gr.Textbox(label="Person’s Name", placeholder="e.g., Alex")
+
+    message = gr.Textbox(label="Message Received", placeholder="Paste the message here...", lines=3)
+
+    with gr.Row():
+        closeness = gr.Dropdown(
+            choices=["Stranger", "Acquaintance", "Friend", "Close Friend", "Partner", "Ex", "Boss", "Teacher"],
+            label="Closeness Level")
+        use_memory = gr.Checkbox(label="Use memory for this person?", value=True)
+
+    sender_traits = gr.Textbox(label="Background on the Sender", placeholder="e.g., Sarcastic, hates small talk...")
+    relationship = gr.Textbox(label="Relationship History", placeholder="e.g., We used to date, now friends")
+    past_events = gr.Textbox(label="Similar Past Situations", placeholder="e.g., Last time I canceled they acted cold")
+    user_feelings = gr.Textbox(label="Your Current Feelings", placeholder="e.g., I feel confused or anxious")
+
+    output = gr.Textbox(label="🧠 Interpretation", lines=6)
+    history_output = gr.Textbox(label="🧠 Memory Log for This Person", lines=10)
+
+    submit_btn = gr.Button("Interpret Message")
+    submit_btn.click(
+        fn=interpret_message,
+        inputs=[message, person_name, closeness, sender_traits, relationship, past_events, user_feelings, use_memory],
+        outputs=[output, history_output]
+    )
+
+    gr.Markdown("---")
+    gr.Markdown("## 🧠 Memory Advisor Chatbot")
+
+    with gr.Row():
+        chatbot_input = gr.Textbox(label="Ask a follow-up question about this person")
+        chatbot_output = gr.Textbox(label="🤖 Advisor Response", lines=6)
+
+    chatbot_btn = gr.Button("Ask Memory Bot")
+    chatbot_btn.click(fn=memory_chat, inputs=[chatbot_input, person_name], outputs=chatbot_output)
+
+app.launch()