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app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from werkzeug.utils import secure_filename
+import os
+import pandas as pd
+import numpy as np
+import joblib
+import re
+import string
+from PIL import Image
+from pymongo import MongoClient
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics.pairwise import cosine_similarity
+import google.generativeai as genai
+import speech_recognition as sr
+import pyttsx3
+
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+# === Configuration ===
+UPLOAD_FOLDER = 'uploads'
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'pdf', 'docx', 'wav', 'mp3'}
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB
+
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+# === Gemini API Config ===
+genai.configure(api_key="AIzaSyC5-RTbNHj7PX7R-8JOwwUxk6RgWDQtfcA")
+text_model = genai.GenerativeModel("gemini-1.5-pro-latest")
+vision_model = genai.GenerativeModel("gemini-1.5-flash")
+
+# === Voice Tools ===
+recognizer = sr.Recognizer()
+engine = pyttsx3.init()
+
+# === Load Dataset ===
+df = pd.read_csv(r"C:\Users\dhanyashree\OneDrive\Desktop\final_substitution.csv")
+df.columns = df.columns.str.lower()
+df.rename(columns={'food label': 'ingredient', 'substitution label': 'substitute'}, inplace=True)
+if 'ingredient' not in df.columns or 'substitute' not in df.columns:
+    raise ValueError("CSV must contain 'ingredient' and 'substitute' columns.")
+
+# === Preprocessing & Model Training ===
+def preprocess_text(text):
+    if isinstance(text, str):
+        text = text.lower()
+        text = re.sub(r'\d+', '', text)
+        text = text.translate(str.maketrans('', '', string.punctuation))
+        text = text.strip()
+    return text
+
+df['ingredient_clean'] = df['ingredient'].apply(preprocess_text)
+df['substitute_clean'] = df['substitute'].apply(preprocess_text)
+
+vectorizer = TfidfVectorizer()
+X = vectorizer.fit_transform(df['ingredient_clean'])
+y = df['substitute_clean']
+
+model_ml = LogisticRegression()
+model_ml.fit(X, y)
+
+# Save models
+joblib.dump(model_ml, "ingredient_substitution_model.pkl")
+joblib.dump(vectorizer, "tfidf_vectorizer.pkl")
+
+# === MongoDB for Recipe Scaling ===
+client = MongoClient("mongodb://localhost:27017/")
+db = client.recipe_db
+
+# === In-memory conversation history ===
+conversation_history = []
+
+# === Allowed File Types ===
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+# === Helper: Ask Gemini Bot ===
+def ask_baking_bot(user_input):
+    prompt = f"""
+    You are a professional pastry chef and baking expert.
+    Only answer questions about baking, pastry, and cooking.
+    User's Question: {user_input}
+    """
+    response = text_model.generate_content(prompt)
+    return response.text
+
+# === Helper: Suggest Substitute ===
+def suggest_substitute(query):
+    query = preprocess_text(query)
+    query_vec = vectorizer.transform([query])
+    try:
+        predicted_substitute = model_ml.predict(query_vec)[0]
+        ai_verification = ask_baking_bot(f"Is {predicted_substitute} a valid substitute for {query}?")
+        if query.lower() in ai_verification.lower():
+            return f"✅ ML & Gemini Verified Substitute: {predicted_substitute}"
+    except:
+        pass
+    similarity_scores = cosine_similarity(query_vec, X)
+    best_match_idx = np.argmax(similarity_scores)
+    return f"🔍 Suggested Substitute (Similarity-Based): {df.iloc[best_match_idx]['substitute_clean']}"
+
+# === Endpoint: Ask Question ===
+@app.route('/api/ask', methods=['POST'])
+def ask_question():
+    data = request.get_json()
+    if not data or 'question' not in data:
+        error_response = {"error": "Missing 'question' in request"}
+        print("❌ Error:", error_response)
+        return jsonify(error_response), 400
+
+    question = data['question']
+    response = ask_baking_bot(question)
+
+    conversation_history.append({"user": question, "assistant": response})
+    response_data = {"question": question, "response": response}
+
+    print("✅ Response to frontend:", response_data)
+    return jsonify(response_data)
+
+# === Endpoint: Analyze Image ===
+@app.route('/api/analyze-image', methods=['POST'])
+def analyze_image_endpoint():
+    if 'file' not in request.files:
+        error_response = {"error": "No file uploaded"}
+        print("❌ Error:", error_response)
+        return jsonify(error_response), 400
+
+    file = request.files['file']
+    if file.filename == '':
+        error_response = {"error": "No selected file"}
+        print("❌ Error:", error_response)
+        return jsonify(error_response), 400
+
+    if file and allowed_file(file.filename):
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(filepath)
+
+        try:
+            analysis = analyze_food_image(filepath)
+            os.unlink(filepath)
+            response_data = {"image": filename, "analysis": analysis}
+            print("📷 Image Analysis Response:", response_data)
+            return jsonify(response_data)
+        except Exception as e:
+            error_response = {"error": str(e)}
+            print("❌ Error:", error_response)
+            return jsonify(error_response), 500
+
+    error_response = {"error": "Invalid file type"}
+    print("❌ Error:", error_response)
+    return jsonify(error_response), 400
+
+# === Helper: Analyze Image with Gemini ===
+def analyze_food_image(image_path):
+    try:
+        image = Image.open(image_path)
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+        prompt = """
+        You are a professional chef and baking expert.
+        Analyze this food image and provide:
+        1. Likely food item
+        2. Key ingredients visible
+        3. Comments on preparation and doneness
+        4. Tip for improvement if needed
+        """
+        response = vision_model.generate_content([prompt, image])
+        return response.text
+    except Exception as e:
+        return f"Error analyzing image: {str(e)}"
+
+# === Run App ===
+if __name__ == '__main__':
+    app.run(debug=True)

ingredient_substitution_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b2a256abf44dfa50892d27ce6d30810993d812229ed0061b157236b3667ebd0e
+size 2848919

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5ae3af8094996956e893987f747481e5c6b3b595c3db00259ea67397943b6f11
+size 15021