Update app.py

app.py

@@ -1,95 +1,103 @@
-import os
-import time
-from flask import Flask, render_template, jsonify, request
-from src.helper import download_hugging_face_embeddings
-from langchain.llms import Replicate
-from dotenv import load_dotenv
-from PyPDF2 import PdfReader
-from langchain.schema import Document
-from langchain.text_splitter import CharacterTextSplitter
-
-# Initialize Flask app
-app = Flask(__name__)
-
-# Load environment variables
-load_dotenv()
-
-# Define the load_pdf function
-""" def load_pdf(file_path):
-    all_text = ""
-    with open(file_path, 'rb') as file:
-        reader = PdfReader(file)
-        for page in reader.pages:
-            all_text += page.extract_text() + "\n"
-    return all_text if all_text else None
- """
-# Define the text_split function
-""" def text_split(text):
-    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-    document = Document(page_content=text)
-    return text_splitter.split_documents([document])
- """
-# Load and process data
-#pdf_file_path = "data/Gale Encyclopedia of Medicine Vol. 1 (A-B).pdf"  # Update this path to your single PDF file
-#extracted_data = load_pdf(pdf_file_path)
-#if extracted_data is None:
-    #raise ValueError("The extracted data is None. Please check the load_pdf function.")
-
-#print(f"Extracted Data: {extracted_data}")
-
-# Split the extracted text into chunks
-#text_chunks = text_split(extracted_data)
-#if not text_chunks:
-    #raise ValueError("The text_chunks is None or empty. Please check the text_split function.")
-
-#print(f"Text Chunks: {text_chunks}")
-
-embeddings = download_hugging_face_embeddings()
-if embeddings is None:
-    raise ValueError("The embeddings is None. Please check the download_hugging_face_embeddings function.")
-
-print(f"Embeddings: {embeddings}")
-
-# Setup CTransformers LLM
-from langchain.llms import Replicate
-os.environ["REPLICATE_API_TOKEN"]= "r8_PArS6Z8LxcRZld6NjVxhd8NJbIJXYDE25XiJd"
-llm = Replicate(model="a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea")
-
-
-# Flask routes
-@app.route("/")
-def index():
-    return render_template('chat.html')
-
-@app.route("/get", methods=["GET", "POST"])
-def chat():
-    try:
-        msg = request.form["msg"]
-        input_text = msg
-        print(f"Received message: {input_text}")
-        
-        # Display spinner
-        result = {"generated_text": "Thinking..."}
-        
-        # Simulate processing delay
-        time.sleep(1)
-        
-        # Retrieve response from the model
-        result = llm.generate([input_text])
-        print(f"LLMResult: {result}")
-        
-        # Access the generated text from the result object
-        if result.generations and result.generations[0]:
-            generated_text = result.generations[0][0].text
-        else:
-            generated_text = "No response generated."
-        
-        print(f"Response: {generated_text}")
-        
-        return str(generated_text)
-    except Exception as e:
-        print(f"Error: {e}")
-        return jsonify({"error": str(e)}), 500
-
-if __name__ == '__main__':
+import os
+import time
+from flask import Flask, render_template, jsonify, request
+from src.helper import download_hugging_face_embeddings
+from langchain.llms import Replicate
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.schema import Document
+from langchain.text_splitter import CharacterTextSplitter
+
+# Initialize Flask app
+app = Flask(__name__)
+
+# Load environment variables
+load_dotenv()
+
+# Define the load_pdf function
+""" def load_pdf(file_path):
+    all_text = ""
+    with open(file_path, 'rb') as file:
+        reader = PdfReader(file)
+        for page in reader.pages:
+            all_text += page.extract_text() + "\n"
+    return all_text if all_text else None
+ """
+# Define the text_split function
+""" def text_split(text):
+    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+    document = Document(page_content=text)
+    return text_splitter.split_documents([document])
+ """
+# Load and process data
+#pdf_file_path = "data/Gale Encyclopedia of Medicine Vol. 1 (A-B).pdf"  # Update this path to your single PDF file
+#extracted_data = load_pdf(pdf_file_path)
+#if extracted_data is None:
+    #raise ValueError("The extracted data is None. Please check the load_pdf function.")
+
+#print(f"Extracted Data: {extracted_data}")
+
+# Split the extracted text into chunks
+#text_chunks = text_split(extracted_data)
+#if not text_chunks:
+    #raise ValueError("The text_chunks is None or empty. Please check the text_split function.")
+
+#print(f"Text Chunks: {text_chunks}")
+
+embeddings = download_hugging_face_embeddings()
+if embeddings is None:
+    raise ValueError("The embeddings is None. Please check the download_hugging_face_embeddings function.")
+
+print(f"Embeddings: {embeddings}")
+
+# Setup CTransformers LLM
+from langchain.llms import Replicate
+# Setup Replicate LLM
+os.environ["REPLICATE_API_TOKEN"] = "r8_PArS6Z8LxcRZld6NjVxhd8NJbIJXYDE25XiJd"
+llm = Replicate(
+    model="a16z-infra/llama7b-v2-chat:4f0a4744c7295c024a1de15e1a63c880d3da035fa1f49bfd344fe076074c8eea",
+    config={
+        'max_new_tokens': 100,  # Maximum number of tokens to generate in response
+        'temperature': 0.7,     # Optimal temperature for balanced randomness and coherence
+        'top_k': 50             # Optimal top-k value for considering the top 50 predictions
+    }
+)
+
+
+# Flask routes
+@app.route("/")
+def index():
+    return render_template('chat.html')
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    try:
+        msg = request.form["msg"]
+        input_text = msg
+        print(f"Received message: {input_text}")
+        
+        # Display spinner
+        result = {"generated_text": "Thinking..."}
+        
+        # Simulate processing delay
+        time.sleep(1)
+        
+        # Retrieve response from the model
+        result = llm.generate([input_text])
+        print(f"LLMResult: {result}")
+        
+        # Access the generated text from the result object
+        if result.generations and result.generations[0]:
+            generated_text = result.generations[0][0].text
+        else:
+            generated_text = "No response generated."
+        
+        print(f"Response: {generated_text}")
+        
+        return str(generated_text)
+    except Exception as e:
+        print(f"Error: {e}")
+        return jsonify({"error": str(e)}), 500
+
+if __name__ == '__main__':
     app.run(host="0.0.0.0", port=8080, debug=True)