chatbot chagnes

backend.py

@@ -24,7 +24,7 @@ from bert import analyze_with_clinicalBert, classify_disease_and_severity, extra
 from disease_links import diseases as disease_links
 from disease_steps import disease_next_steps
 from disease_support import disease_doctor_specialty, disease_home_care
-from past_reports import router as reports_router
+from past_reports import router as reports_router, db_fetch_reports
 
 model = genai.GenerativeModel('gemini-1.5-flash')
 df = pd.read_csv("measurement.csv")
@@ -96,6 +96,7 @@ except Exception as e:
     raise RuntimeError(f"Failed to configure Firebase: {e}")
 
 class ChatRequest(BaseModel):
+    user_id: str
     question: str
 
 class ChatResponse(BaseModel):
@@ -159,16 +160,7 @@ def ocr_text_from_image(image_bytes: bytes) -> str:
     print(response_text)
 
     return response_text
-
-@app.post("/chat/", response_model=ChatResponse)
-async def chat_endpoint(request: ChatRequest):
-    """
-    Chatbot endpoint that answers questions based on the last analyzed document and user history.
-    """
-    global EXTRACTED_TEXT_CACHE
-    if not EXTRACTED_TEXT_CACHE:
-        raise HTTPException(status_code=400, detail="Please provide a document context by analyzing text first.")
-    
+def get_past_reports_from_firestore(user_id: str):
     try:
         reports_ref = db.collection('users').document(request.user_id).collection('reports')
         docs = reports_ref.order_by('timestamp', direction=firestore.Query.DESCENDING).limit(10).stream()
@@ -179,8 +171,35 @@ async def chat_endpoint(request: ChatRequest):
             history_text += f"Report from {report_data.get('timestamp', 'N/A')}:\n{report_data.get('ocr_text', 'No OCR text found')}\n\n"
     except Exception as e:
         history_text = "No past reports found for this user."
-    
+    return history_text
+
+def get_past_reports_from_sqllite(user_id: str):
+    try:
+        reports = db_fetch_reports(user_id=user_id, limit=10, offset=0)
+        
+        history_text = ""
+        for report in reports:
+            history_text += f"Report from {report.get('report_date', 'N/A')}:\n{report.get('ocr_text', 'No OCR text found')}\n\n"
+    except Exception as e:
+        history_text = "No past reports found for this user."
+    return history_text
+
+@app.post("/chat/", response_model=ChatResponse)
+async def chat_endpoint(request: ChatRequest):
+    """
+    Chatbot endpoint that answers questions based on the last analyzed document and user history.
+    """
+    print("Received chat request for user:", request.user_id)
+    #history_text = get_past_reports_from_firestore(request.user_id)
+    history_text = get_past_reports_from_sqllite(request.user_id)
+
     full_document_text = EXTRACTED_TEXT_CACHE + "\n\n" + "PAST REPORTS:\n" + history_text
+
+    if not full_document_text:
+        raise HTTPException(status_code=400, detail="No past reports or current data exists for this user")
+    
+    
+   
     
     try:
         full_prompt = system_prompt_chat.format(

web/analyzer.html

@@ -231,11 +231,11 @@
           accept=".pdf, image/*"
           class="w-full mb-4 rounded px-3 py-2"
         />
-        <input
+      <!-- <input
           type="date"
           id="report-date"
           class="w-full mb-4 rounded px-3 py-2"
-        />
+        /> -->
         <button id="analyze-btn" class="btn-primary px-4 py-2 rounded">
           Analyze with AI
         </button>
@@ -451,15 +451,15 @@
         .getElementById("analyze-btn")
         .addEventListener("click", async () => {
           const file = document.getElementById("pdf-upload").files[0];
-          const date = document.getElementById("report-date").value;
+          //const date = document.getElementById("report-date").value;
           if (!file) {
             loadingEl.textContent = "Please upload a file first.";
             return;
           }
-          if (!date) {
+          /*if (!date) {
             loadingEl.textContent = "Please select the report date.";
             return;
-          }
+          }*/
 
           loadingEl.textContent = "Processing with AI...";
           textOutput.textContent = "";
@@ -519,7 +519,7 @@
 
             await postReportToBackend({
               user_id: currentUser.email,
-              report_date: date,
+              report_date: new Date(),
               ocr_text: extractedText,
               anomalies: JSON.stringify(recs),
               measurements: JSON.stringify(findings),