Update app.py

app.py

@@ -6,149 +6,120 @@ import shutil
 import os
 from huggingface_hub import InferenceClient
 import json
+from langchain_community.agent_toolkits import GmailToolkit
+
 
 # Initialize FastAPI app
 app = FastAPI()
 
-# Class labels
-class_labels = {
-    0: 'Baked Potato', 1: 'Burger', 2: 'Crispy Chicken', 3: 'Donut', 4: 'Fries',
-    5: 'Hot Dog', 6: 'Jalapeno', 7: 'Kiwi', 8: 'Lemon', 9: 'Lettuce',
-    10: 'Mango', 11: 'Onion', 12: 'Orange', 13: 'Pizza', 14: 'Taquito', 
-    15: 'Apple', 16: 'Banana', 17: 'Beetroot', 18: 'Bell Pepper', 19: 'Bread',
-    20: 'Cabbage', 21: 'Carrot', 22: 'Cauliflower', 23: 'Cheese',
-    24: 'Chilli Pepper', 25: 'Corn', 26: 'Crab', 27: 'Cucumber',
-    28: 'Eggplant', 29: 'Eggs', 30: 'Garlic', 31: 'Ginger', 32: 'Grapes',
-    33: 'Milk', 34: 'Salmon', 35: 'Yogurt'
-}
-
-# Load the trained model
-model = tf.keras.models.load_model("model_unfreezeNewCorrectpredict.keras")
-
-# Image preprocessing function
-def load_and_prep_image(file_path, img_shape=224):
-    img = tf.io.read_file(file_path)
-    img = tf.image.decode_image(img, channels=3)
-    img = tf.image.resize(img, size=[img_shape, img_shape])
-    img = tf.expand_dims(img, axis=0)
-    return img
-
-# Predict label function
-def predict_label(model, image_path, class_names):
-    img = load_and_prep_image(image_path, img_shape=224)
-    pred = model.predict(img)
-    pred_class_index = np.argmax(pred, axis=1)[0]
-    pred_class_name = class_names[pred_class_index]
-    return pred_class_name
-
-
 @app.get("/")
-def read_root():
-    return {"message": "This is My Nutrionguid App"}
-
-# API endpoint for prediction
-@app.post("/predict")
-async def predict_image(file: UploadFile = File(...)):
-    try:
-        # Save the uploaded file
-        file_location = f"./temp_{file.filename}"
-        with open(file_location, "wb") as f:
-            shutil.copyfileobj(file.file, f)
+def read_root
+    toolkit = GmailToolkit()
+    return {"message": "Connection"}
+
+# # API endpoint for prediction
+# @app.post("/predict")
+# async def predict_image(file: UploadFile = File(...)):
+#     try:
+#         # Save the uploaded file
+#         file_location = f"./temp_{file.filename}"
+#         with open(file_location, "wb") as f:
+#             shutil.copyfileobj(file.file, f)
         
-        # Predict the label
-        prediction = predict_label(model, file_location, class_labels)
+#         # Predict the label
+#         prediction = predict_label(model, file_location, class_labels)
         
-        # Remove the temporary file
-        os.remove(file_location)
+#         # Remove the temporary file
+#         os.remove(file_location)
         
-        return {"predicted_label": prediction}
-    except Exception as e:
-        return JSONResponse(
-            status_code=500,
-            content={"error": f"An error occurred: {str(e)}"}
-        )
-@app.post("/predictNUT")
-async def predict_image_and_nutrition(file: UploadFile = File(...)):
-    try:
-        # Save the uploaded file
-        file_location = f"./temp_{file.filename}"
-        with open(file_location, "wb") as f:
-            shutil.copyfileobj(file.file, f)
+#         return {"predicted_label": prediction}
+#     except Exception as e:
+#         return JSONResponse(
+#             status_code=500,
+#             content={"error": f"An error occurred: {str(e)}"}
+#         )
+# @app.post("/predictNUT")
+# async def predict_image_and_nutrition(file: UploadFile = File(...)):
+#     try:
+#         # Save the uploaded file
+#         file_location = f"./temp_{file.filename}"
+#         with open(file_location, "wb") as f:
+#             shutil.copyfileobj(file.file, f)
         
-        # Predict the label using the same prediction logic
-        prediction = predict_label(model, file_location, class_labels)
+#         # Predict the label using the same prediction logic
+#         prediction = predict_label(model, file_location, class_labels)
         
-        # Remove the temporary file
-        os.remove(file_location)
-
-        # Define the repository ID and your token
-        #repo_id = "google/gemma-2-9b-it"
-        repo_id = "Qwen/Qwen2.5-72B-Instruct"
-        # repo_id = "microsoft/Phi-3-mini-4k-instruct"
-        #repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-        api_token = "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21"
-
-        # Initialize the InferenceClient with your token
-        llm_client = InferenceClient(
-            model=repo_id,
-            token=api_token[:-2],  # Pass the token here
-            timeout=120,
-        )
-
-        # Function to call the LLM
-        def call_llm(inference_client: InferenceClient, prompt: str):
-            response = inference_client.post(
-                json={
-                    "inputs": prompt,
-                    "parameters": {"max_new_tokens": 500},
-                    "task": "text-generation",
-                },
-            )
-            return json.loads(response.decode())[0]["generated_text"]
+#         # Remove the temporary file
+#         os.remove(file_location)
+
+#         # Define the repository ID and your token
+#         #repo_id = "google/gemma-2-9b-it"
+#         repo_id = "Qwen/Qwen2.5-72B-Instruct"
+#         # repo_id = "microsoft/Phi-3-mini-4k-instruct"
+#         #repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+#         api_token = "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21"
+
+#         # Initialize the InferenceClient with your token
+#         llm_client = InferenceClient(
+#             model=repo_id,
+#             token=api_token[:-2],  # Pass the token here
+#             timeout=120,
+#         )
+
+#         # Function to call the LLM
+#         def call_llm(inference_client: InferenceClient, prompt: str):
+#             response = inference_client.post(
+#                 json={
+#                     "inputs": prompt,
+#                     "parameters": {"max_new_tokens": 500},
+#                     "task": "text-generation",
+#                 },
+#             )
+#             return json.loads(response.decode())[0]["generated_text"]
         
-        # Use the prediction to generate nutrition information
-        # prompt = f"Nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} in formatted list"
-        # # prompt = f"Provide all the nutrition information for {prediction}, including Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, and Pantothenic acid. Please present the information in a clear, formatted list only, without additional explanations."
-        # response = call_llm(llm_client, prompt)
+#         # Use the prediction to generate nutrition information
+#         # prompt = f"Nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} in formatted list"
+#         # # prompt = f"Provide all the nutrition information for {prediction}, including Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, and Pantothenic acid. Please present the information in a clear, formatted list only, without additional explanations."
+#         # response = call_llm(llm_client, prompt)
 
-        # return {"predicted_label": prediction, "nutrition_info": response}
+#         # return {"predicted_label": prediction, "nutrition_info": response}
 
-        # nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} per 100 grams in a formatted list only."
-        nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} per 100 grams, Output the information as a concise, formatted list without repetition."
-        nutrition_info = call_llm(llm_client, nutrition_prompt)
+#         # nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6, Folate, Niacin, Pantothenic acid) for {prediction} per 100 grams in a formatted list only."
+#         nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} per 100 grams, Output the information as a concise, formatted list without repetition."
+#         nutrition_info = call_llm(llm_client, nutrition_prompt)
         
-        # # Second prompt: Health benefits and tips
-        health_benefits_prompt = f"Provide the health benefits and considerations for {prediction}. Additionally, include practical tips for making {prediction} healthier. Keep the response focused on these two aspects only."
-        # health_benefits_prompt = f"Provide detailed information about {prediction}, including its origin, common uses, cultural significance, and any interesting facts. Keep the response informative and well-structured."
-        Information = call_llm(llm_client, health_benefits_prompt)
+#         # # Second prompt: Health benefits and tips
+#         health_benefits_prompt = f"Provide the health benefits and considerations for {prediction}. Additionally, include practical tips for making {prediction} healthier. Keep the response focused on these two aspects only."
+#         # health_benefits_prompt = f"Provide detailed information about {prediction}, including its origin, common uses, cultural significance, and any interesting facts. Keep the response informative and well-structured."
+#         Information = call_llm(llm_client, health_benefits_prompt)
         
-        recipes_prompt=f"Tell me about the two most famous recipes for {prediction}. Include the ingredients only."
-        recipes_info=call_llm(llm_client, recipes_prompt)
+#         recipes_prompt=f"Tell me about the two most famous recipes for {prediction}. Include the ingredients only."
+#         recipes_info=call_llm(llm_client, recipes_prompt)
 
-        return {
-            "Predicted_label": prediction, 
-            "Nutrition_info": nutrition_info,
-            "Information": Information,
-            "Recipes":recipes_info
-        }
-    except Exception as e:
-        return JSONResponse(
-            status_code=500,
-            content={"error": f"An error occurred: {str(e)}"}
-        )
+#         return {
+#             "Predicted_label": prediction, 
+#             "Nutrition_info": nutrition_info,
+#             "Information": Information,
+#             "Recipes":recipes_info
+#         }
+#     except Exception as e:
+#         return JSONResponse(
+#             status_code=500,
+#             content={"error": f"An error occurred: {str(e)}"}
+#         )
 
 
 
 
-        #nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} in a formatted list only."
-        # nutrition_info = call_llm(llm_client, nutrition_prompt)
+#         #nutrition_prompt = f"Provide the nutrition information (Calories, Protein, Carbohydrates, Dietary Fiber, Sugars, Fat, Sodium, Potassium, Vitamin C, Vitamin B6) for {prediction} in a formatted list only."
+#         # nutrition_info = call_llm(llm_client, nutrition_prompt)
 
-        # # Second prompt: Health benefits and tips
-        # health_benefits_prompt = f"Provide the health benefits and considerations for {prediction} and give tips for making it healthier."
-        # health_benefits_and_tips = call_llm(llm_client, health_benefits_prompt)
+#         # # Second prompt: Health benefits and tips
+#         # health_benefits_prompt = f"Provide the health benefits and considerations for {prediction} and give tips for making it healthier."
+#         # health_benefits_and_tips = call_llm(llm_client, health_benefits_prompt)
 
-        # return {
-        #     "predicted_label": prediction, 
-        #     "nutrition_info": nutrition_info,
-        #     "health_benefits_and_tips": health_benefits_and_tips
-        # }
+#         # return {
+#         #     "predicted_label": prediction, 
+#         #     "nutrition_info": nutrition_info,
+#         #     "health_benefits_and_tips": health_benefits_and_tips
+#         # }