Update app.py

app.py

@@ -262,137 +262,150 @@
 # if __name__ == '__main__':
 #     main()
 
+
+
+
 import streamlit as st
-import os
-import re
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from PyPDF2 import PdfReader
-from peft import get_peft_model, LoraConfig, TaskType
-
-# ✅ Force CPU execution
-device = torch.device("cpu")
-
-# 🔹 Load IBM Granite Model (CPU-Compatible)
-MODEL_NAME = "ibm-granite/granite-3.1-2b-instruct"
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_NAME,
-    device_map="cpu",  # Force CPU execution
-    torch_dtype=torch.float32  # Use float32 since Hugging Face runs on CPU
-)
-
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-
-# 🔹 Apply LoRA Fine-Tuning Configuration
-lora_config = LoraConfig(
-    r=8,
-    lora_alpha=32,
-    target_modules=["q_proj", "v_proj"],
-    lora_dropout=0.1,
-    bias="none",
-    task_type=TaskType.CAUSAL_LM
-)
-model = get_peft_model(model, lora_config)
-model.eval()
-
-# 🛠 Function to Read & Extract Text from PDFs
-def read_files(file):
-    file_context = ""
-    reader = PdfReader(file)
+
+st.title("File Upload Debugging")
+
+uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")
+
+if uploaded_file:
+    st.success(f"File uploaded: {uploaded_file.name}")
+    st.write(f"File Size: {uploaded_file.size / 1024:.2f} KB")
+
+# import streamlit as st
+# import os
+# import re
+# import torch
+# from transformers import AutoModelForCausalLM, AutoTokenizer
+# from PyPDF2 import PdfReader
+# from peft import get_peft_model, LoraConfig, TaskType
+
+# # ✅ Force CPU execution
+# device = torch.device("cpu")
+
+# # 🔹 Load IBM Granite Model (CPU-Compatible)
+# MODEL_NAME = "ibm-granite/granite-3.1-2b-instruct"
+
+# model = AutoModelForCausalLM.from_pretrained(
+#     MODEL_NAME,
+#     device_map="cpu",  # Force CPU execution
+#     torch_dtype=torch.float32  # Use float32 since Hugging Face runs on CPU
+# )
+
+# tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+# # 🔹 Apply LoRA Fine-Tuning Configuration
+# lora_config = LoraConfig(
+#     r=8,
+#     lora_alpha=32,
+#     target_modules=["q_proj", "v_proj"],
+#     lora_dropout=0.1,
+#     bias="none",
+#     task_type=TaskType.CAUSAL_LM
+# )
+# model = get_peft_model(model, lora_config)
+# model.eval()
+
+# # 🛠 Function to Read & Extract Text from PDFs
+# def read_files(file):
+#     file_context = ""
+#     reader = PdfReader(file)
     
-    for page in reader.pages:
-        text = page.extract_text()
-        if text:
-            file_context += text + "\n"
+#     for page in reader.pages:
+#         text = page.extract_text()
+#         if text:
+#             file_context += text + "\n"
     
-    return file_context.strip()
-
-# 🛠 Function to Format AI Prompts
-def format_prompt(system_msg, user_msg, file_context=""):
-    if file_context:
-        system_msg += f" The user has provided a contract document. Use its context to generate insights, but do not repeat or summarize the document itself."
-    return [
-        {"role": "system", "content": system_msg},
-        {"role": "user", "content": user_msg}
-    ]
-
-# 🛠 Function to Generate AI Responses
-def generate_response(input_text, max_tokens=1000, top_p=0.9, temperature=0.7):
-    model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
+#     return file_context.strip()
+
+# # 🛠 Function to Format AI Prompts
+# def format_prompt(system_msg, user_msg, file_context=""):
+#     if file_context:
+#         system_msg += f" The user has provided a contract document. Use its context to generate insights, but do not repeat or summarize the document itself."
+#     return [
+#         {"role": "system", "content": system_msg},
+#         {"role": "user", "content": user_msg}
+#     ]
+
+# # 🛠 Function to Generate AI Responses
+# def generate_response(input_text, max_tokens=1000, top_p=0.9, temperature=0.7):
+#     model_inputs = tokenizer([input_text], return_tensors="pt").to(device)
     
-    with torch.no_grad():
-        output = model.generate(
-            **model_inputs,
-            max_new_tokens=max_tokens,
-            do_sample=True,
-            top_p=top_p,
-            temperature=temperature,
-            num_return_sequences=1,
-            pad_token_id=tokenizer.eos_token_id
-        )
+#     with torch.no_grad():
+#         output = model.generate(
+#             **model_inputs,
+#             max_new_tokens=max_tokens,
+#             do_sample=True,
+#             top_p=top_p,
+#             temperature=temperature,
+#             num_return_sequences=1,
+#             pad_token_id=tokenizer.eos_token_id
+#         )
     
-    return tokenizer.decode(output[0], skip_special_tokens=True)
-
-# 🛠 Function to Clean AI Output
-def post_process(text):
-    cleaned = re.sub(r'戥+', '', text)  # Remove unwanted symbols
-    lines = cleaned.splitlines()
-    unique_lines = list(dict.fromkeys([line.strip() for line in lines if line.strip()]))
-    return "\n".join(unique_lines)
-
-# 🛠 Function to Handle RAG with IBM Granite & Streamlit
-def granite_simple(prompt, file):
-    file_context = read_files(file) if file else ""
+#     return tokenizer.decode(output[0], skip_special_tokens=True)
+
+# # 🛠 Function to Clean AI Output
+# def post_process(text):
+#     cleaned = re.sub(r'戥+', '', text)  # Remove unwanted symbols
+#     lines = cleaned.splitlines()
+#     unique_lines = list(dict.fromkeys([line.strip() for line in lines if line.strip()]))
+#     return "\n".join(unique_lines)
+
+# # 🛠 Function to Handle RAG with IBM Granite & Streamlit
+# def granite_simple(prompt, file):
+#     file_context = read_files(file) if file else ""
     
-    system_message = "You are IBM Granite, a legal AI assistant specializing in contract analysis."
+#     system_message = "You are IBM Granite, a legal AI assistant specializing in contract analysis."
     
-    messages = format_prompt(system_message, prompt, file_context)
-    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+#     messages = format_prompt(system_message, prompt, file_context)
+#     input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
     
-    response = generate_response(input_text)
-    return post_process(response)
-
-# 🔹 Streamlit UI
-def main():
-    st.set_page_config(page_title="Contract Analysis AI", page_icon="📜")
-
-    st.title("📜 AI-Powered Contract Analysis Tool")
-    st.write("Upload a contract document (PDF) for a detailed AI-driven legal and technical analysis.")
-
-    # 🔹 Sidebar Settings
-    with st.sidebar:
-        st.header("⚙️ Settings")
-        max_tokens = st.slider("Max Tokens", 50, 1000, 250, 50)
-        top_p = st.slider("Top P (sampling)", 0.1, 1.0, 0.9, 0.1)
-        temperature = st.slider("Temperature (creativity)", 0.1, 1.0, 0.7, 0.1)
-
-    # 🔹 File Upload Section
-    uploaded_file = st.file_uploader("📂 Upload a contract document (PDF)", type="pdf")
-
-    # ✅ Ensure file upload message is displayed
-    if uploaded_file is not None:
-        st.session_state["uploaded_file"] = uploaded_file  # Persist file in session state
-        st.success("✅ File uploaded successfully!")
-        st.write("Click the button below to analyze the contract.")
-
-        # Force button to always render
-        st.markdown('<style>div.stButton > button {display: block; width: 100%;}</style>', unsafe_allow_html=True)
-
-        if st.button("🔍 Analyze Document"):
-            with st.spinner("Analyzing contract document... ⏳"):
-                final_answer = granite_simple(
-                    "Perform a detailed technical analysis of the attached contract document, highlighting potential risks, legal pitfalls, compliance issues, and areas where contractual terms may lead to future disputes or operational challenges.",
-                    uploaded_file
-                )
-
-            # 🔹 Display Analysis Result
-            st.subheader("📑 Analysis Result")
-            st.write(final_answer)
-
-# 🔥 Run Streamlit App
-if __name__ == '__main__':
-    main()
+#     response = generate_response(input_text)
+#     return post_process(response)
+
+# # 🔹 Streamlit UI
+# def main():
+#     st.set_page_config(page_title="Contract Analysis AI", page_icon="📜")
+
+#     st.title("📜 AI-Powered Contract Analysis Tool")
+#     st.write("Upload a contract document (PDF) for a detailed AI-driven legal and technical analysis.")
+
+#     # 🔹 Sidebar Settings
+#     with st.sidebar:
+#         st.header("⚙️ Settings")
+#         max_tokens = st.slider("Max Tokens", 50, 1000, 250, 50)
+#         top_p = st.slider("Top P (sampling)", 0.1, 1.0, 0.9, 0.1)
+#         temperature = st.slider("Temperature (creativity)", 0.1, 1.0, 0.7, 0.1)
+
+#     # 🔹 File Upload Section
+#     uploaded_file = st.file_uploader("📂 Upload a contract document (PDF)", type="pdf")
+
+#     # ✅ Ensure file upload message is displayed
+#     if uploaded_file is not None:
+#         st.session_state["uploaded_file"] = uploaded_file  # Persist file in session state
+#         st.success("✅ File uploaded successfully!")
+#         st.write("Click the button below to analyze the contract.")
+
+#         # Force button to always render
+#         st.markdown('<style>div.stButton > button {display: block; width: 100%;}</style>', unsafe_allow_html=True)
+
+#         if st.button("🔍 Analyze Document"):
+#             with st.spinner("Analyzing contract document... ⏳"):
+#                 final_answer = granite_simple(
+#                     "Perform a detailed technical analysis of the attached contract document, highlighting potential risks, legal pitfalls, compliance issues, and areas where contractual terms may lead to future disputes or operational challenges.",
+#                     uploaded_file
+#                 )
+
+#             # 🔹 Display Analysis Result
+#             st.subheader("📑 Analysis Result")
+#             st.write(final_answer)
+
+# # 🔥 Run Streamlit App
+# if __name__ == '__main__':
+#     main()