|
|
@@ -30,8 +30,47 @@ import time
|
|
|
|
|
|
import threading
|
|
|
|
|
|
+def strip_scratchpad(text: str) -> str:
|
|
|
+ import re
|
|
|
+ # Strip out the XML <scratchpad> tag and everything in between, non-greedily
|
|
|
+ clean_text = re.sub(r'<scratchpad>.*?</scratchpad>', '', text, flags=re.DOTALL)
|
|
|
+ return clean_text.strip()
|
|
|
+
|
|
|
+def filter_scratchpad_stream(stream):
|
|
|
+ buffer = ""
|
|
|
+ in_scratchpad = False
|
|
|
+ for chunk in stream:
|
|
|
+ content = chunk['message']['content']
|
|
|
+ buffer += content
|
|
|
+
|
|
|
+ while True:
|
|
|
+ if not in_scratchpad:
|
|
|
+ start_idx = buffer.find("<scratchpad>")
|
|
|
+ if start_idx != -1:
|
|
|
+ yield buffer[:start_idx]
|
|
|
+ buffer = buffer[start_idx:]
|
|
|
+ in_scratchpad = True
|
|
|
+ else:
|
|
|
+ yield_len = max(0, len(buffer) - 11)
|
|
|
+ if yield_len > 0:
|
|
|
+ yield buffer[:yield_len]
|
|
|
+ buffer = buffer[yield_len:]
|
|
|
+ break
|
|
|
+ else:
|
|
|
+ end_idx = buffer.find("</scratchpad>")
|
|
|
+ if end_idx != -1:
|
|
|
+ buffer = buffer[end_idx + 13:]
|
|
|
+ in_scratchpad = False
|
|
|
+ else:
|
|
|
+ keep_len = 12
|
|
|
+ if len(buffer) > keep_len:
|
|
|
+ buffer = buffer[-keep_len:]
|
|
|
+ break
|
|
|
+ if not in_scratchpad and buffer:
|
|
|
+ yield buffer
|
|
|
+
|
|
|
def pull_model_bg():
|
|
|
- try: ollama.pull('llama3.2:3b')
|
|
|
+ try: ollama.pull('qwen2.5:7b')
|
|
|
except: pass
|
|
|
threading.Thread(target=pull_model_bg, daemon=True).start()
|
|
|
|
|
|
@@ -415,7 +454,7 @@ with tab_chat:
|
|
|
|
|
|
try:
|
|
|
temp_messages = [{"role": "system", "content": sys_prompt}] + [m for m in st.session_state.messages if m["role"] != "tool"]
|
|
|
- response_stream = ollama.chat(model='llama3.2:3b', messages=temp_messages, stream=True)
|
|
|
+ response_stream = ollama.chat(model='qwen2.5:7b', messages=temp_messages, stream=True)
|
|
|
|
|
|
with st.chat_message("assistant"):
|
|
|
ai_reply = st.write_stream(chunk['message']['content'] for chunk in response_stream)
|
|
|
@@ -622,7 +661,7 @@ with tab_explore:
|
|
|
minimal_records = df_display[['product_name', 'Medical Warning']].head(10).to_dict('records')
|
|
|
eval_prompt = f"The user has this profile: {profile_text}. Evaluate these top foods and state which are highly recommended or strictly forbidden: {minimal_records}. Provide a direct, readable clinical summary. Do not output raw JSON."
|
|
|
try:
|
|
|
- response_stream = ollama.chat(model='llama3.2:3b', messages=[{'role': 'user', 'content': eval_prompt}], stream=True)
|
|
|
+ response_stream = ollama.chat(model='qwen2.5:7b', messages=[{'role': 'user', 'content': eval_prompt}], stream=True)
|
|
|
st.write_stream(chunk['message']['content'] for chunk in response_stream)
|
|
|
except Exception as e:
|
|
|
error_msg = str(e).lower()
|
|
|
@@ -818,22 +857,33 @@ with tab_planner:
|
|
|
Dietary constraint: {diet_pref}. Additional notes: {extra_notes}.
|
|
|
Health profile: {profile_text}.
|
|
|
|
|
|
- CRITICAL INSTRUCTIONS:
|
|
|
- - You MUST formulate the menu using ONLY the following real database items retrieved for you: {db_context}
|
|
|
- - Output the menu beautifully formatted as a Markdown Table.
|
|
|
- - The Markdown table MUST strictly contain 5 columns separated by pipes (|).
|
|
|
- - Columns MUST be exactly: | Meal Time | Exact Food | Portion Size | Calories | Protein |
|
|
|
- - If you merge columns, the system will fail. Separate Calories and Protein with a pipe.
|
|
|
- - Do NOT output JSON. Do NOT use tool calls.
|
|
|
- - Provide a direct answer. Skip all thinking, reasoning, and pleasantries.
|
|
|
+ COGNITIVE SCRATCHPAD INSTRUCTIONS:
|
|
|
+ - You MUST perform all your intermediate thinking, unit conversions (e.g. converting cups, tablespoons, or ounces to exact metric grams based on food density), and calorie/protein mathematical additions inside a `<scratchpad>` tag.
|
|
|
+ - Format:
|
|
|
+ <scratchpad>
|
|
|
+ Calculations:
|
|
|
+ - 1.5 cups of Cheese = X grams (density Y). Calories = A, Protein = B.
|
|
|
+ - 2 tbsp of Peanut Butter = Z grams (density C). Calories = D, Protein = E.
|
|
|
+ - Summation: Total Calories = A + D = Z kcal (vs target {target_cal}kcal). Total Protein = B + E = Fg.
|
|
|
+ </scratchpad>
|
|
|
+ | Meal Time | Exact Food | Portion Size | Calories | Protein |
|
|
|
+ | --- | --- | --- | --- | --- |
|
|
|
+ ...
|
|
|
+
|
|
|
+ CRITICAL FORMATTING INSTRUCTIONS:
|
|
|
+ - After the </scratchpad> closing tag, you MUST strictly output the menu formatted as a Markdown Table.
|
|
|
+ - The table MUST contain exactly 5 columns separated by pipes (|): | Meal Time | Exact Food | Portion Size | Calories | Protein |
|
|
|
+ - The items in the table MUST be selected strictly from: {db_context}
|
|
|
+ - Do NOT output JSON. Do NOT use tool calls. Skip pleasantries.
|
|
|
"""
|
|
|
|
|
|
temp_messages = [{'role': 'system', 'content': sys_prompt}, {'role': 'user', 'content': 'Generate my meal plan as a markdown table.'}]
|
|
|
|
|
|
# Stream the response instantly!
|
|
|
try:
|
|
|
- response_stream = ollama.chat(model='llama3.2:3b', messages=temp_messages, stream=True)
|
|
|
- ai_reply = st.write_stream(chunk['message']['content'] for chunk in response_stream)
|
|
|
+ response_stream = ollama.chat(model='qwen2.5:7b', messages=temp_messages, stream=True)
|
|
|
+ clean_stream = filter_scratchpad_stream(response_stream)
|
|
|
+ ai_reply = st.write_stream(clean_stream)
|
|
|
|
|
|
# PDF Generation
|
|
|
def generate_pdf(text):
|
|
|
@@ -916,7 +966,7 @@ with tab_planner:
|
|
|
|
|
|
st.download_button(
|
|
|
label="📄 Download PDF Export",
|
|
|
- data=generate_pdf(ai_reply),
|
|
|
+ data=generate_pdf(strip_scratchpad(ai_reply)),
|
|
|
file_name="Clinical_Meal_Plan.pdf",
|
|
|
mime="application/pdf",
|
|
|
type="primary"
|
