Update app.py

app.py

@@ -1,102 +1,62 @@
-import os
-import fitz  # PyMuPDF
-import numpy as np
-import faiss
-from sentence_transformers import SentenceTransformer
+# app.py
 import gradio as gr
-import spaces  # for ZeroGPU
-
-@spaces.GPU
-def query_app(user_input, include_source, verbose):
-    return search_index(user_input, index, documents, include_source, verbose)
-
-# PDF reader
-def extract_text_from_pdf(folder_path="meal_plans"):
-    documents = []
-    for filename in os.listdir(folder_path):
-        if filename.lower().endswith(".pdf"):
-            path = os.path.join(folder_path, filename)
-            try:
-                doc = fitz.open(path)
-                text = ""
-                for page in doc:
-                    text += page.get_text()
-                documents.append({"text": text, "source": filename})
-            except Exception as e:
-                print(f"Error reading {filename}: {e}")
-    return documents
-
-# Index builder
-def create_index(docs):
-    texts = [doc["text"] for doc in docs]
-    embeddings = model.encode(texts)
-    dim = embeddings[0].shape[0]
-    index = faiss.IndexFlatL2(dim)
-    index.add(np.array(embeddings).astype("float32"))
-    return index
-
-# Search logic
-def search_index(query, index, docs, include_source=True, verbose=False, top_k=3):
-    query_vec = model.encode([query])
-    D, I = index.search(np.array(query_vec).astype("float32"), top_k)
-    responses = []
-    for i in I[0]:
-        doc = docs[i]
-        snippet = doc["text"][:750 if verbose else 300].replace("\n", " ").strip()
-        label = f"**📄 {doc['source']}**\n" if include_source else ""
-        responses.append(f"{label}{snippet}...")
-    return "\n\n---\n\n".join(responses)
-
-# Setup
-model = SentenceTransformer("all-MiniLM-L6-v2")
-documents = extract_text_from_pdf("meal_plans")
-index = create_index(documents)
-
-def ingest(folder_path="meal_plans", index_file="mealplan.index"):
-    if os.path.exists(index_file) and os.path.exists("docstore.npy"):
-        print("📦 Loading existing FAISS index...")
-        index = faiss.read_index(index_file)
-        documents = np.load("docstore.npy", allow_pickle=True).tolist()
-    else:
-        print("📥 Ingesting PDFs from scratch...")
-        documents = extract_text_from_pdf(folder_path)
-        texts = [doc["text"] for doc in documents]
-        embeddings = model.encode(texts)
-        dim = embeddings[0].shape[0]
-        index = faiss.IndexFlatL2(dim)
-        index.add(np.array(embeddings).astype("float32"))
-
-        faiss.write_index(index, index_file)
-        np.save("docstore.npy", documents)
-        print("✅ Index and documents saved.")
-
-    return documents, index
-
-# Load model
-model = SentenceTransformer("all-MiniLM-L6-v2")
+import spaces
+from transformers import pipeline
+import os
+from PyPDF2 import PdfReader
+from dotenv import load_dotenv
 
-# Ingest PDFs and build/load index
-documents, index = ingest("meal_plans")
+load_dotenv()
 
-# Gradio UI
-with gr.Blocks(title="Meal Plan Chat Assistant") as demo:
-    gr.Markdown("## 🍽️ Meal Plan Assistant\nChat with your PDF documents in `meal_plans/` folder.")
+@spaces.GPU
+class ChatBot:
+    def __init__(self):
+        self.llm = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.1", token=os.getenv("HF_TOKEN"))
+        self.context = ""
+
+    def read_meal_plans(self, folder="meal_plans"):
+        text = ""
+        for file in os.listdir(folder):
+            if file.endswith(".pdf"):
+                reader = PdfReader(os.path.join(folder, file))
+                for page in reader.pages:
+                    text += page.extract_text() + "\n"
+        return text
+
+    def reply(self, message, history, preferences):
+        diet, goal, allergens = preferences
+        if not self.context:
+            mealplan_text = self.read_meal_plans()
+            self.context = f"Meal Plans: {mealplan_text}\nUser Preferences: Diet={diet}, Goal={goal}, Allergens={allergens}"
+
+        prompt = f"{self.context}\nUser: {message}\nAI:"
+        response = self.llm(prompt, max_new_tokens=100, do_sample=True, temperature=0.7)[0]['generated_text'].split("AI:")[-1].strip()
+        return response
+
+bot = ChatBot()
+
+def chat(message, history, diet, goal, allergens):
+    return bot.reply(message, history, (diet, goal, allergens))
+
+diet_choices = ["Vegetarian", "Vegan", "Keto", "Paleo", "No Preference"]
+goal_choices = ["Weight Loss", "Muscle Gain", "Maintenance"]
+allergen_choices = ["Nuts", "Dairy", "Gluten", "Soy", "Eggs"]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# 🥗 AI Meal Plan Assistant")
     with gr.Row():
-        with gr.Column(scale=4):
-            chatbot = gr.Chatbot()
-            user_input = gr.Textbox(placeholder="Ask something...", show_label=False)
-            send_btn = gr.Button("Ask")
-        with gr.Column(scale=1):
-            include_source = gr.Checkbox(label="Include Source", value=True)
-            verbose = gr.Checkbox(label="Verbose Mode", value=False)
-
-    def user_query(msg, history, source, verbose_mode):
-        answer = query_app(msg, source, verbose_mode)
-        history = history + [(msg, answer)]
-        return history, history
-
-    send_btn.click(user_query, 
-                   inputs=[user_input, chatbot, include_source, verbose], 
-                   outputs=[chatbot, chatbot])
+        diet = gr.Dropdown(diet_choices, label="Diet Type")
+        goal = gr.Dropdown(goal_choices, label="Goal")
+        allergens = gr.CheckboxGroup(allergen_choices, label="Allergies")
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(placeholder="Ask me for meal ideas...", label="Message")
+    send = gr.Button("Send")
+
+    def user_input(message, chat_history):
+        response = chat(message, chat_history, diet.value, goal.value, allergens.value)
+        chat_history.append((message, response))
+        return chat_history, ""
+
+    send.click(user_input, [msg, chatbot], [chatbot, msg])
 
 demo.launch()