Initial Commit.

app.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb 13 11:22:52 2024
+
+@author: stinpankajm
+"""
+import os
+import base64
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
+
+# Formats the prompt to hold all of the past messages
+def format_prompt(message, history):
+    prompt = "<s>"
+    prompt_template = "[INST] {} [/INST]"
+
+    # Iterates through every past user input and response to be added to the prompt
+    for user_prompt, bot_response in history:
+        prompt += prompt_template.format(user_prompt)
+        prompt += f" {bot_response}</s> "
+        
+    prompt += prompt_template.format(message)
+    return prompt
+
+MODEL_PATH = "/home/stinpankajm/workspace/FG_POCs/Insects_Scouting/Models/F_Scout_v0.2"
+
+
+css = """
+#warning {background-color: #FFCCCB} 
+#flag {color: red;}
+#topHeading {
+    padding: 30px 0px 30px 15px;
+    box-shadow: 1px 0px 30px 0px rgba(0, 0, 0, 0.1);
+}
+#logoImg {
+    max-width: 260px;
+}
+"""
+
+
+# Use for GEC, Doesn't track actual history
+def format_prompt_finadvisor(message, history):
+    prompt = "<s>"
+
+    # String to add before every prompt
+    prompt_prefix = """\
+        You are an agriculture expert providing advice to farmers and users.
+
+        Your task is to answer questions related to agriculture based on the Question provided below.
+
+        Do not provide any explanations and respond only with medium short answers, add bullet points whenever necessary..
+
+        Your TEXT to analyze:
+        """
+    
+    prompt_template = "[INST] " + prompt_prefix + ' {} [/INST]'
+
+
+    # Iterates through every past user input and response to be added to the prompt
+    for user_prompt, bot_response in history:
+        prompt += prompt_template.format(user_prompt)
+        prompt += f" {bot_response}</s> \n"
+
+    prompt += prompt_template.format(message)
+    return prompt
+
+
+def generate(prompt, history, system_prompt, temperature=0.9, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0):
+    temperature = float(temperature)
+    if temperature < 1e-2: temperature = 1e-2
+    top_p = float(top_p)
+    
+    generate_kwargs = dict(temperature=temperature, max_new_tokens=max_new_tokens, top_p=top_p, repetition_penalty=repetition_penalty, do_sample=True, seed=42,)
+    
+    #formatted_prompt = format_prompt_grammar(f"Corrected Sentence: {prompt}", history)
+    formatted_prompt = format_prompt_finadvisor(f"{system_prompt} {prompt}", history)
+    # print("\nPROMPT: \n\t" + formatted_prompt)
+
+    # Generate text from the HF inference
+    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    output = ""
+
+    for response in stream:
+        output += response.token.text
+        yield output
+    return output
+
+additional_inputs=[
+    gr.Textbox( label="System Prompt", value="" , max_lines=1, interactive=True, ),
+    gr.Slider( label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs", ),
+    gr.Slider( label="Max new tokens", value=256, minimum=0, maximum=1048, step=64, interactive=True, info="The maximum numbers of new tokens", ),
+    gr.Slider( label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1, step=0.05, interactive=True, info="Higher values sample more low-probability tokens", ),
+    gr.Slider( label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05, interactive=True, info="Penalize repeated tokens", )
+]
+
+with gr.Blocks(css=css) as demo:
+    """
+        Top Custom Header 
+    """
+    with gr.Row(elem_id="topHeading"):
+        # with gr.Column(elem_id="logoImg"):
+        #     with open('./static/logo.jpeg', "rb") as image:
+        #         encoded = base64.b64encode(image.read()).decode()
+        #         logo_image = f"data:image/png;base64,{encoded}"
+
+        #         gr.HTML(f'<img src={logo_image} style="width:155px">')
+
+            # gr.Image(Image.open('./static/FarmGyan logo_1.png'))
+        with gr.Column():
+            gr.Markdown(
+                """
+            # FinAdvisor
+            """,
+            )
+
+    """
+        Model Prediction
+    """
+    
+
+
+gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    additional_inputs=additional_inputs,
+    title="AgExpert",
+    examples=[],
+).queue().launch()
+# ).queue().launch(auth=("shivraiAdmin", "FarmERP@2024"), auth_message="Please enter your credentials to get started.")
+# demo.launch(show_api=False)

requirements.txt

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+gradio
+pytorch
+transformers
+datasets