Update app.py

app.py

@@ -1,54 +1,54 @@
 import os
+os.system('pip install torch')  # or 'pip install tensorflow'
 os.system('pip install transformers')
 os.system('pip install datasets')
+os.system('pip install gradio')
+os.system('pip install minijinja')
+os.system('pip install PyMuPDF')
 
 import gradio as gr
 from huggingface_hub import InferenceClient
 from transformers import pipeline
 from datasets import load_dataset
+import fitz  # PyMuPDF
 
-dataset = load_dataset("ibunescu/qa_legal_dataset_train")
-
-# Use a pipeline as a high-level helper
-pipe = pipeline("fill-mask", model="nlpaueb/legal-bert-base-uncased")
-
-"""
-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
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+client = InferenceClient()
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+dataset = load_dataset("ibunescu/qa_legal_dataset_train")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def score_argument_from_outcome(outcome, argument):
+    prosecutor_score = 0
+    if "Prosecutor" in outcome:
+        prosecutor_score = outcome.count("Prosecutor") * 2
+        if "won" in outcome and "Prosecutor" in outcome:
+            prosecutor_score += 10
+    return prosecutor_score
+
+def chat_between_bots(system_message1, system_message2, max_tokens, temperature, top_p, history1, history2, shared_history, message):
+    response1, history1 = list(respond(message, history1, system_message1, max_tokens, temperature, top_p))[-1]
+    response2, history2 = list(respond(message, history2, system_message2, max_tokens, temperature, top_p))[-1]
+    
+    return response1, response2, history1, history2, shared_history
 
-    messages.append({"role": "user", "content": message})
+def extract_text_from_pdf(pdf_file):
+    text = ""
+    doc = fitz.open(pdf_file)
+    for page in doc:
+        text += page.get_text()
+    return text
 
+def ask_about_pdf(pdf_text, question):
+    prompt = f"PDF Content: {pdf_text}\n\nQuestion: {question}\n\nAnswer:"
     response = ""
-
     for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
+        [{"role": "system", "content": "You are a legal expert answering questions based on the PDF content provided."},
+         {"role": "user", "content": prompt}],
+        max_tokens=512,
         stream=True,
-        temperature=temperature,
-        top_p=top_p,
+        temperature=0.6,
+        top_p=0.95,
     ):
         token = message.choices[0].delta.content
-
-        response += token
-        yield response
-        token = message.choices[0].delta.content
         if token is not None:
             response += token
     return response