Update app.py

app.py

@@ -1,6 +1,15 @@
 # app.py
+
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.stem import WordNetLemmatizer
 import gradio as gr
 
+# — Download NLTK resources if they’re missing —
+#    (Only needs to be run once per environment)
+nltk.download("punkt")
+nltk.download("wordnet")
+
 forward_rules = {
     "cold": ["Aconitum napellus", "Allium cepa"],
     "fever": ["Gelsemium", "Belladonna", "Ferrum phosphoricum"],
@@ -80,10 +89,10 @@ forward_rules = {
     "copd": ["Arsenicum album", "Bryonia alba"],
     "pneumonia": ["Antimonium tartaricum", "Bryonia alba"],
     "influenza": ["Influenzinum", "Gelsemium sempervirens"],
-    "tonsillitis": ["Belladonna", "Mercurius solubilis"],
-    "gingivitis": ["Mercurius solubilis", "Arsenicum album"],
-    "halitosis": ["Mercurius solubilis", "Carbo vegetabilis"],
-    "oral thrush": ["Borax", "Mercurius solubilis"],
+    "tonsillitis": ["Belladonna", "Mercurius solubil"],
+    "gingivitis": ["Mercurius solubil", "Arsenicum album"],
+    "halitosis": ["Mercurius solubil", "Carbo vegetabilis"],
+    "oral thrush": ["Borax", "Mercurius solubil"],
     "shingles": ["Rhus toxicodendron", "Mezereum"],
     "chickenpox": ["Rhus toxicodendron", "Antimonium tartaricum"],
     "measles": ["Euphrasia officinalis", "Aconitum napellus"],
@@ -104,54 +113,74 @@ forward_rules = {
 }
 
 def make_tag(text, bgcolor="#e0f7fa", fgcolor="#006064"):
-    return f'<span style="display:inline-block; background-color:{bgcolor}; '\
-           f'color:{fgcolor}; padding:4px 8px; border-radius:12px; '\
-           f'margin:2px; font-weight:600;">{text}</span>'
+    return (
+        f'<span style="display:inline-block; background-color:{bgcolor}; '
+        f'color:{fgcolor}; padding:4px 8px; border-radius:12px; '
+        f'margin:2px; font-weight:600;">{text}</span>'
+    )
+
+lemmatizer = WordNetLemmatizer()
+
+def preprocess(text: str):
+    """
+    Tokenize + lemmatize, just in case you want lemmatized tokens
+    for future extensions. We won’t use this for exact phrase matching,
+    but it may help if you want to add more advanced NLP logic later.
+    """
+    tokens = word_tokenize(text.lower())
+    lemmas = [lemmatizer.lemmatize(token) for token in tokens]
+    return lemmas
 
-def homeopathy_bot(user_input):
-    ui = user_input.strip().lower()
-    found = False
+def homeopathy_bot(user_input: str) -> str:
+    """
+    1. Try forward lookup: check if any symptom‐phrase is literally a substring
+       of user_input.lower()
+    2. If not found, do reverse lookup: check if any remedy‐phrase is in user_input.lower()
+    """
+    raw = user_input.lower()
     html_chunks = []
 
-    # Forward lookup
+    # 1) Forward lookup (symptom → remedies)
     for symptom, meds in forward_rules.items():
-        if symptom in ui:
+        if symptom in raw:
             html_chunks.append(f"<p>💊 Suggested medicines for {make_tag(symptom)}:</p>")
-            med_tags = " ".join([make_tag(m, bgcolor="#e8f5e9", fgcolor="#1b5e20") for m in meds])
+            med_tags = " ".join(
+                make_tag(m, bgcolor="#e8f5e9", fgcolor="#1b5e20") for m in meds
+            )
             html_chunks.append(f"<p>{med_tags}</p>")
-            found = True
-            break
+            return "\n".join(html_chunks)  # Once matched, return immediately
 
-    # Reverse lookup
-    if not found:
-        related = []
-        med_found = ""
-        for symptom, meds in forward_rules.items():
-            for med in meds:
-                if med.lower() in ui:
-                    related.append(symptom)
-                    med_found = med
-        if related:
-            html_chunks.append(f"<p>{make_tag(med_found, bgcolor='#ffe0b2', fgcolor='#e65100')} is used for:</p>")
-            unique_symptoms = sorted(set(related))
-            symptom_tags = " ".join([make_tag(s) for s in unique_symptoms])
-            html_chunks.append(f"<p>{symptom_tags}</p>")
-            found = True
-
-    if not found:
+    # 2) Reverse lookup (remedy → symptoms)
+    #    Loop through every (symptom, [meds]) pair and see if any med is in raw.
+    related_symptoms = []
+    med_found = ""
+    for symptom, meds in forward_rules.items():
+        for med in meds:
+            if med.lower() in raw:
+                related_symptoms.append(symptom)
+                med_found = med
+    if related_symptoms:
         html_chunks.append(
-            "<p style='color:#b71c1c; font-weight:600;'>"
-            "❌ Sorry, I couldn’t find any matches. Try another symptom or medicine.</p>"
+            f"<p>{make_tag(med_found, bgcolor='#ffe0b2', fgcolor='#e65100')} is used for:</p>"
         )
+        unique_symptoms = sorted(set(related_symptoms))
+        symptom_tags = " ".join(make_tag(s) for s in unique_symptoms)
+        html_chunks.append(f"<p>{symptom_tags}</p>")
+        return "\n".join(html_chunks)
 
+    # 3) If nothing matches
+    html_chunks.append(
+        "<p style='color:#b71c1c; font-weight:600;'>"
+        "❌ Sorry, I couldn’t find any matches. Try another symptom or medicine.</p>"
+    )
     return "\n".join(html_chunks)
 
-
+# Build the Gradio interface
 demo = gr.Interface(
     fn=homeopathy_bot,
     inputs=gr.Textbox(
         lines=2,
-        placeholder="Type a symptom/disease (e.g., 'cold', 'headache') or a medicine name...",
+        placeholder="Type a symptom/disease (e.g., 'cold', 'headache') or a medicine name…",
         label="Your Query"
     ),
     outputs=gr.HTML(label="Response"),
@@ -159,9 +188,11 @@ demo = gr.Interface(
     description=(
         "Enter a symptom/disease to get suggested homeopathic remedies, "
         "or enter a medicine name to see which conditions it’s used for. "
+        "Results appear as colorful tags!"
     ),
     theme="huggingface",
     allow_flagging="never"   # ← disable the 🏴 flag button
 )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()