Update app.py

app.py

@@ -1,58 +1,65 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
-import os
 
-# Load your token from Hugging Face Secrets in your Space settings
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-# Choose a good open-source chat model
+# ✅ This model supports chat completions through the free Inference API
 MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
 
-client = InferenceClient(model=MODEL_NAME, token=HF_TOKEN)
-
-SYSTEM_PROMPT = """
-You are a friendly recipe assistant 🍳. 
-You suggest recipes based on:
-- Ingredients the user has
-- Their budget
-- Any allergies or dietary restrictions
-- Cooking difficulty level they prefer
-Always give clear step-by-step instructions.
-"""
-
-# Chat function
-def chat(user_message, history):
-    # Convert history into messages format
-    messages = [{"role": "system", "content": SYSTEM_PROMPT}]
-    
-    for user, bot in history:
-        messages.append({"role": "user", "content": user})
-        messages.append({"role": "assistant", "content": bot})
-    
-    messages.append({"role": "user", "content": user_message})
-    
-    # Get response from model
-    response = client.chat_completion(
-        messages=messages,
-        max_tokens=512,
-        temperature=0.7,
-        top_p=0.9
+client = InferenceClient(model=MODEL_NAME)
+
+# Example fake recipe database
+recipes = [
+    {
+        "name": "Veggie Pasta",
+        "ingredients": ["pasta", "tomato", "garlic", "olive oil"],
+        "allergies": ["gluten"],
+        "budget": "low"
+    },
+    {
+        "name": "Chicken Stir Fry",
+        "ingredients": ["chicken", "soy sauce", "broccoli", "garlic"],
+        "allergies": ["soy"],
+        "budget": "medium"
+    }
+]
+
+def find_recipes(budget, have_items, allergies):
+    results = []
+    for recipe in recipes:
+        if recipe["budget"] != budget:
+            continue
+        if any(a in recipe["allergies"] for a in allergies):
+            continue
+        if not all(item in have_items for item in recipe["ingredients"]):
+            continue
+        results.append(recipe["name"])
+    return results or ["No matching recipes found."]
+
+def chatbot(message, history, budget, have_items, allergies):
+    recipes_found = find_recipes(budget, have_items.split(","), allergies.split(","))
+    system_prompt = (
+        f"You are a friendly cooking assistant. The user has budget '{budget}', "
+        f"these ingredients: {have_items}, and allergies: {allergies}. "
+        f"Suggest recipes from this list: {recipes_found}."
     )
-    
-    reply = response.choices[0].message["content"]
-    history.append((user_message, reply))
-    return history, ""
 
-# UI
+    msgs = [{"role": "system", "content": system_prompt}]
+    for user_msg, bot_msg in history:
+        msgs.append({"role": "user", "content": user_msg})
+        msgs.append({"role": "assistant", "content": bot_msg})
+    msgs.append({"role": "user", "content": message})
+
+    response_text = ""
+    for resp in client.chat_completion(messages=msgs, stream=True):
+        token = resp.choices[0].delta.content or ""
+        response_text += token
+        yield response_text
+
 with gr.Blocks() as demo:
-    gr.Markdown("# 🍽️ Smart Recipe Chatbot")
-    chatbot = gr.Chatbot()
-    msg = gr.Textbox(label="Ask me for a recipe...")
-    clear = gr.Button("Clear Chat")
-    
-    msg.submit(chat, [msg, chatbot], [chatbot, msg])
-    clear.click(lambda: ([], ""), None, [chatbot, msg])
-
-# Run
-if __name__ == "__main__":
-    demo.launch()
+    gr.Markdown("## 🍳 Recipe Suggestion Chatbot")
+
+    budget = gr.Dropdown(["low", "medium", "high"], label="Budget", value="low")
+    have_items = gr.Textbox(label="Ingredients you have (comma separated)", placeholder="pasta,tomato,garlic")
+    allergies = gr.Textbox(label="Allergies (comma separated)", placeholder="gluten,soy")
+    chatbot_ui = gr.ChatInterface(fn=lambda msg, hist: chatbot(msg, hist, budget.value, have_items.value, allergies.value))
+
+demo.launch()