Update app.py

app.py

@@ -88,35 +88,31 @@ if prompt := st.chat_input():
     user_sentiment = TextBlob(prompt).sentiment.polarity
 
     # Check if the query is relevant to the drug-side effects dataset
-    if not is_relevant_query(prompt):
-        response = "I'm sorry, but your question seems to be out of my scope. Please ask about drugs, side effects, or related medical conditions."
-    else:
-        # Craft System Prompt based on sentiment
-        system_prompt = SYSTEM_PROMPT_GENERAL
-        if user_sentiment < 0:  # User expresses negative sentiment
-            system_prompt = f"""{system_prompt} 
-            The user seems to be feeling down. Prioritize empathetic responses and open-ended questions."""
-
-        # Format prompt using LangChain's PromptTemplate
-        formatted_prompt = prompt_template.format(
-            system_prompt=system_prompt,
-            user_input=prompt
-        )
-
-        # Generate a response using Hugging Face API
-        response = ""
-        for message in client.chat_completion(
-            messages=[{"role": "user", "content": formatted_prompt}],
-            max_tokens=500,
-            stream=True,
-        ):
-            response += message.choices[0].delta.content
-
-        # Process response for specific tokens
-        if "Ashley:" in response:
-            response = response.split("Treasure:")[1].strip()
-        elif "User:" in response:
-            response = response.split("Assistant:")[1].strip()
+    system_prompt = SYSTEM_PROMPT_GENERAL
+    if user_sentiment < 0:  # User expresses negative sentiment
+        system_prompt = f"""{system_prompt} 
+        The user seems to be feeling down. Prioritize empathetic responses and open-ended questions."""
+
+    # Format prompt using LangChain's PromptTemplate
+    formatted_prompt = prompt_template.format(
+        system_prompt=system_prompt,
+        user_input=prompt
+    )
+
+    # Generate a response using Hugging Face API
+    response = ""
+    for message in client.chat_completion(
+        messages=[{"role": "user", "content": formatted_prompt}],
+        max_tokens=500,
+        stream=True,
+    ):
+        response += message.choices[0].delta.content
+
+    # Process response for specific tokens
+    if "Ashley:" in response:
+        response = response.split("Treasure:")[1].strip()
+    elif "User:" in response:
+        response = response.split("Assistant:")[1].strip()
 
     # Append assistant message to the session state
     st.session_state.messages.append({"role": "assistant", "content": response.strip()})