init

app.py

@@ -1,62 +1,39 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+import pandas as pd
+import google.generativeai as genai
+
+import kagglehub
+path = kagglehub.dataset_download("fahmidachowdhury/food-adulteration-dataset")
+gemapi = userdata.get("AIzaSyAmDOBWfGuEju0oZyUIcn_H0k8XW0cTP7k")
+genai.configure(api_key = gemapi)
+
+import os
+os.listdir(path)
+path = path + "/"+ os.listdir(path)[0]
+
+
+# Initializing Model:
+system_instruction = f"""
+You are a public assistant who specializes in food safety. You look at data and explain to the user any question they ask; here is your data {str(data.to_json())}\
+You are also a food expert in Indian context. You act as the representative of the Goverment  or public agencies always keeping the needs of the people to the forefront.
+You will try to help the customer launch a feedback review whenever they complain. You are to prepare a "markdown" report which is detailed and which can be sent to the company or restaurant.\
+In case of a complaint or a grievance, You will act like a detective gathering necessary information from the user untill you are satisfied; Once You gather all the info, you are supposed to generate a markdown report\
+Once the customer asks you to show them the markdown report, you will use the information given to you to generate a report.\
+You will ask the customer a single question at a time, which is relevent and you will not repeat another question until youve generated the report.
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
+model_path = "gemini-1.5-flash"
+FoodSafetyAssistant = genai.GenerativeModel(model_path, system_instruction = system_instruction)
 
-    response = ""
+def startChat(usertxt, chat_history=[]):
+  while usertxt != "exit" or usertxt != "Exit":
+    chat = FoodSafetyAssistant.start_chat(history = chat_history)
+    response = chat.send_message(usertxt)
+    yield response.text
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    respond
 )