Update App.py

App.py

@@ -1,41 +1,42 @@
-import gradio as gr
 import pandas as pd
+import gradio as gr
 
-# Load food data CSV
+# Load the extended food data
 df = pd.read_csv("food_data_extended.csv")
 
-# Lowercase for consistent matching
+# Convert food names to lowercase for matching
 df["food"] = df["food"].str.lower()
 
-def analyze_foods(input_text):
-    input_items = [item.strip().lower() for item in input_text.split(",")]
-
-    matched = df[df["food"].isin(input_items)]
-
-    if matched.empty:
-        return "❌ Koi bhi food item match nahi hua. Dobara try karein."
-
-    result = ""
-    for _, row in matched.iterrows():
-        result += (
-            f"🍽️ **{row['food'].title()}**\n"
-            f"  🔹 Calories: {row['calories']} kcal\n"
-            f"  🔹 Protein: {row['protein']} g\n"
-            f"  🔹 Carbs: {row['carbs']} g\n"
-            f"  🔹 Fat: {row['fat']} g\n\n"
-        )
-    return result
-
-title = "🥗 NutriTrack AI - Food Nutrient Analyzer"
-description = "Kisi bhi food item ka naam likhein (1 ya zyada, comma se alag karein) aur iske calories, protein, carbs aur fat ka accurate data paayein. Example: `egg, milk, rice`"
-
-iface = gr.Interface(
+# Nutrient search function
+def analyze_foods(food_query):
+    food_query = food_query.lower()
+    items = [item.strip() for item in food_query.split(",")]
+
+    results = []
+
+    for item in items:
+        match = df[df["food"].str.contains(item)]
+        if not match.empty:
+            results.append(match)
+        else:
+            results.append(pd.DataFrame([{
+                "food": item,
+                "calories": "Not found",
+                "protein": "Not found",
+                "carbs": "Not found",
+                "fat": "Not found"
+            }]))
+
+    final = pd.concat(results)
+    return final.reset_index(drop=True)
+
+# Gradio UI
+app = gr.Interface(
     fn=analyze_foods,
-    inputs=gr.Textbox(lines=2, placeholder="e.g. egg, chicken, rice"),
-    outputs=gr.Markdown(),
-    title=title,
-    description=description,
-    allow_flagging="never"
+    inputs=gr.Textbox(label="Enter food items (comma-separated)", placeholder="e.g. apple, rice, chicken biryani"),
+    outputs=gr.Dataframe(label="Nutritional Information"),
+    title="🍎 NutriTrack AI - Food Nutrient Analyzer",
+    description="Type any food(s) to get calories, protein, carbs & fat. Supports 200+ food items. Try: banana, pizza, milk, apple"
 )
 
-iface.launch()
+app.launch()